human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "sparse then dense alignment based 3d map reconstruction method for endoscopic capsule robots"
  Abstract: Despite significant progress achieved in the last decade to convert passive capsule endoscopes to actively controllable robots, robotic capsule endoscopy still has some challenges. In particular, a fully dense three-dimensional (3D) map reconstruction of the explored organ remains an unsolved problem. Such a dense map would help doctors detect the locations and sizes of the diseased areas more reliably, resulting in more accurate diagnoses. In this study, we propose a comprehensive medical 3D reconstruction method for endoscopic capsule robots, which is built in a modular fashion including preprocessing, keyframe selection, sparse-then-dense alignment-based pose estimation, bundle fusion, and shading-based 3D reconstruction. A detailed quantitative analysis is performed using a non-rigid esophagus gastroduodenoscopy simulator, four different endoscopic cameras, a magnetically activated soft capsule robot, a sub-millimeter precise optical motion tracker, and a fine-scale 3D optical scanner, whereas qualitative ex-vivo experiments are performed on a porcine pig stomach. To the best of our knowledge, this study is the first complete endoscopic 3D map reconstruction approach containing all of the necessary functionalities for a therapeutically relevant 3D map reconstruction.

Connected through hyperedge HE-1 (induced by source paper "reconstructing sinus anatomy from endoscopic video towards a radiation free approach for quantitative longitudinal assessment"):
  with members:
    - "extremely dense point correspondences using a learned feature descriptor"
    - "defslam tracking and mapping of deforming scenes from monocular sequences"
    - "dense depth estimation in monocular endoscopy with self supervised learning methods"
    - "recurrent neural network for un supervised learning of monocular videovisual odometry and depth"
    - "mis slam real time large scale dense deformable slam system in minimal invasive surgery based on heterogeneous computing"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-2 (induced by source paper "a survey of deep learning techniques for mobile robot applications"):
  with members:
    - "unsupervised odometry and depth learning for endoscopic capsule robots"
    - "deep endovo a recurrent convolutional neural network rcnn based visual odometry approach for endoscopic capsule robots"
    - "magnetic visual sensor fusion based dense 3d reconstruction and localization for endoscopic capsule robots"
    - "endo vmfusenet deep visual magnetic sensor fusion approach for uncalibrated unsynchronized and asymmetric endoscopic capsule robot localization data"
    - "endosensorfusion particle filtering based multi sensory data fusion with switching state space model for endoscopic capsule robots"
    - "a fully dense and globally consistent 3d map reconstruction approach for gi tract to enhance therapeutic relevance of the endoscopic capsule robot"
    - "a non rigid map fusion based direct slam method for endoscopic capsule robots"
    - "a deep learning based 6 degree of freedom localization method for endoscopic capsule robots"

Connected through hyperedge HE-3 (induced by source paper "artificial intelligence and its role in near future"):
  with members:
    - "unsupervised odometry and depth learning for endoscopic capsule robots"
    - "deep endovo a recurrent convolutional neural network rcnn based visual odometry approach for endoscopic capsule robots"
    - "magnetic visual sensor fusion based dense 3d reconstruction and localization for endoscopic capsule robots"
    - "endo vmfusenet deep visual magnetic sensor fusion approach for uncalibrated unsynchronized and asymmetric endoscopic capsule robot localization data"
    - "endosensorfusion particle filtering based multi sensory data fusion with switching state space model for endoscopic capsule robots"
    - "a fully dense and globally consistent 3d map reconstruction approach for gi tract to enhance therapeutic relevance of the endoscopic capsule robot"
    - "a non rigid map fusion based direct slam method for endoscopic capsule robots"
    - "a deep learning based 6 degree of freedom localization method for endoscopic capsule robots"

Connected through hyperedge HE-4 (induced by source paper "ganvo unsupervised deep monocular visual odometry and depth estimation with generative adversarial networks"):
  with members:
    - "learning monocular visual odometry with dense 3d mapping from dense 3d flow"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "unsupervised odometry and depth learning for endoscopic capsule robots"
    - "deep endovo a recurrent convolutional neural network rcnn based visual odometry approach for endoscopic capsule robots"
    - "magnetic visual sensor fusion based dense 3d reconstruction and localization for endoscopic capsule robots"
    - "endo vmfusenet deep visual magnetic sensor fusion approach for uncalibrated unsynchronized and asymmetric endoscopic capsule robot localization data"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"

Connected through hyperedge HE-5 (induced by source paper "deep endovo a recurrent convolutional neural network rcnn based visual odometry approach for endoscopic capsule robots"):
  with members:
    - "a fully dense and globally consistent 3d map reconstruction approach for gi tract to enhance therapeutic relevance of the endoscopic capsule robot"
    - "a non rigid map fusion based direct slam method for endoscopic capsule robots"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "six degree of freedom localization of endoscopic capsule robots using recurrent neural networks embedded into a convolutional neural network"
    - "a deep learning based 6 degree of freedom localization method for endoscopic capsule robots"
    - "image based localization using lstms for structured feature correlation"
    - "deep residual learning for image recognition"
    - "posenet a convolutional network for real time 6 dof camera relocalization"

Connected through hyperedge HE-6 (induced by source paper "unsupervised odometry and depth learning for endoscopic capsule robots"):
  with members:
    - "deep endovo a recurrent convolutional neural network rcnn based visual odometry approach for endoscopic capsule robots"
    - "endo vmfusenet deep visual magnetic sensor fusion approach for uncalibrated unsynchronized and asymmetric endoscopic capsule robot localization data"
    - "unsupervised learning of depth and ego motion from video"
    - "endosensorfusion particle filtering based multi sensory data fusion with switching state space model for endoscopic capsule robots"
    - "a fully dense and globally consistent 3d map reconstruction approach for gi tract to enhance therapeutic relevance of the endoscopic capsule robot"
    - "a non rigid map fusion based direct slam method for endoscopic capsule robots"
    - "six degree of freedom localization of endoscopic capsule robots using recurrent neural networks embedded into a convolutional neural network"
    - "sfm net learning of structure and motion from video"

Connected through hyperedge HE-7 (induced by source paper "deep visual odometry methods for mobile robots"):
  with members:
    - "unsupervised odometry and depth learning for endoscopic capsule robots"
    - "deep endovo a recurrent convolutional neural network rcnn based visual odometry approach for endoscopic capsule robots"
    - "magnetic visual sensor fusion based dense 3d reconstruction and localization for endoscopic capsule robots"
    - "endo vmfusenet deep visual magnetic sensor fusion approach for uncalibrated unsynchronized and asymmetric endoscopic capsule robot localization data"
    - "direct monocular odometry using points and lines"
    - "endosensorfusion particle filtering based multi sensory data fusion with switching state space model for endoscopic capsule robots"
    - "a fully dense and globally consistent 3d map reconstruction approach for gi tract to enhance therapeutic relevance of the endoscopic capsule robot"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "rgb depth slam review"):
  with members:
    - "unsupervised odometry and depth learning for endoscopic capsule robots"
    - "deep endovo a recurrent convolutional neural network rcnn based visual odometry approach for endoscopic capsule robots"
    - "magnetic visual sensor fusion based dense 3d reconstruction and localization for endoscopic capsule robots"
    - "endo vmfusenet deep visual magnetic sensor fusion approach for uncalibrated unsynchronized and asymmetric endoscopic capsule robot localization data"
    - "endosensorfusion particle filtering based multi sensory data fusion with switching state space model for endoscopic capsule robots"
    - "a fully dense and globally consistent 3d map reconstruction approach for gi tract to enhance therapeutic relevance of the endoscopic capsule robot"
    - "a non rigid map fusion based direct slam method for endoscopic capsule robots"
    - "a deep learning based 6 degree of freedom localization method for endoscopic capsule robots"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.CV(Computer Vision and Pattern Recognition)