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: "a non rigid map fusion based direct slam method for endoscopic capsule robots"
  Abstract: Since the development of capsule endoscopy technology, medical device companies and research groups have made significant progress to turn passive capsule endoscopes into robotic active capsule endoscopes. However, the use of robotic capsules in endoscopy still has some challenges. One such challenge is the precise localization of the actively controlled robot in real-time. In this paper, we propose a non-rigid map fusion based direct simultaneous localization and mapping method for endoscopic capsule robots. The proposed method achieves high accuracy for extensive evaluations of pose estimation and map reconstruction performed on a non-rigid, realistic surgical EsophagoGastroDuodenoscopy Simulator and outperforms state-of-the art methods.

Connected through hyperedge HE-1 (induced by source paper "structured coupled generative adversarial networks for unsupervised monocular depth estimation"):
  with members:
    - "refine and distill exploiting cycle inconsistency and knowledge distillation for unsupervised monocular depth estimation"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "adadepth unsupervised content congruent adaptation for depth estimation"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "monocular depth estimation using multi scale continuous crfs as sequential deep networks"
    - "learning monocular depth by distilling cross domain stereo networks"

Connected through hyperedge HE-2 (induced by source paper "an observable time series based slam algorithm for deforming environment"):
  with members:
    - "mis slam real time large scale dense deformable slam system in minimal invasive surgery based on heterogeneous computing"
    - "slam based quasi dense reconstruction for minimally invasive surgery scenes"
    - "orbslam based endoscope tracking and 3d reconstruction"
    - "volumedeform real time volumetric non rigid reconstruction"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-3 (induced by source paper "efficient two step optimization for large embedded deformation graph based slam"):
  with members:
    - "mis slam real time large scale dense deformable slam system in minimal invasive surgery based on heterogeneous computing"
    - "slam based quasi dense reconstruction for minimally invasive surgery scenes"
    - "orbslam based endoscope tracking and 3d reconstruction"
    - "volumedeform real time volumetric non rigid reconstruction"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-4 (induced by source paper "defslam tracking and mapping of deforming scenes from monocular sequences"):
  with members:
    - "volumedeform real time volumetric non rigid reconstruction"
    - "direct sparse odometry"
    - "template based monocular 3d shape recovery using laplacian meshes"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-5 (induced by source paper "a survey of deep learning techniques for mobile robot applications"):
  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"
    - "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 deep learning based 6 degree of freedom localization method for endoscopic capsule robots"

Connected through hyperedge HE-6 (induced by source paper "artificial intelligence and its role in near future"):
  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"
    - "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 deep learning based 6 degree of freedom localization method for endoscopic capsule robots"

Connected through hyperedge HE-7 (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"
    - "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-8 (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"
    - "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"

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)