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: "six degree of freedom localization of endoscopic capsule robots using recurrent neural networks embedded into a convolutional neural network"
  Abstract: Since its development, ingestible wireless endoscopy is considered to be a painless diagnostic method to detect a number of diseases inside GI tract. Medical related engineering companies have made significant improvements in this technology in last decade; however, some major limitations still residue. Localization of the next generation steerable endoscopic capsule robot in six-degree-of freedom (6 DoF) and active motion control are some of these limitations. The significance of localization capability concerns with the correct diagnosis of the disease area. This paper presents a very robust 6-DoF localization method based on supervised training of an architecture consisting of recurrent networks (RNN) embedded into a convolutional neural network (CNN) to make use of both just-in-moment information obtained by CNN and correlative information across frames obtained by RNN. To our knowledge, the idea of embedding RNNs into a CNN architecture is for the first time proposed in literature. The experimental results show that the proposed RNN-in-CNN architecture performs very well for endoscopic capsule robot localization in cases reflection distortions, noise, sudden camera movements and lack of distinguishable features.

Connected through hyperedge HE-1 (induced by source paper "deep endovo a recurrent convolutional neural network rcnn based visual odometry approach for endoscopic capsule robots"):
  with members:
    - "sparse then dense alignment based 3d map reconstruction method 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"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "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-2 (induced by source paper "unsupervised odometry and depth learning for endoscopic capsule robots"):
  with members:
    - "sparse then dense alignment based 3d map reconstruction method for endoscopic capsule robots"
    - "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"
    - "sfm net learning of structure and motion from video"

Connected through hyperedge HE-3 (induced by source paper "deep visual odometry methods for mobile robots"):
  with members:
    - "sparse then dense alignment based 3d map reconstruction method for endoscopic capsule robots"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "magnetic visual sensor fusion based dense 3d reconstruction and localization for endoscopic capsule robots"):
  with members:
    - "sparse then dense alignment based 3d map reconstruction method for endoscopic capsule robots"
    - "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"
    - "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"
    - "orbslam based endoscope tracking and 3d reconstruction"

Connected through hyperedge HE-5 (induced by source paper "sparse then dense alignment based 3d map reconstruction method for endoscopic capsule robots"):
  with members:
    - "deep endovo a recurrent convolutional neural network rcnn based visual odometry approach 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"
    - "computer aided detection of polyps in optical colonoscopy images"

Connected through hyperedge HE-6 (induced by source paper "endo vmfusenet deep visual magnetic sensor fusion approach for uncalibrated unsynchronized and asymmetric endoscopic capsule robot localization data"):
  with members:
    - "sparse then dense alignment based 3d map reconstruction method for endoscopic capsule robots"
    - "deep endovo a recurrent convolutional neural network rcnn based visual odometry approach for endoscopic capsule robots"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "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"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "a fully dense and globally consistent 3d map reconstruction approach for gi tract to enhance therapeutic relevance of the endoscopic capsule robot"):
  with members:
    - "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)