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: "defslam tracking and mapping of deforming scenes from monocular sequences"
  Abstract: We present the first monocular SLAM capable of operating in deforming scenes in real-time. Our DefSLAM approach fuses isometric Shape-from-Template (SfT) and Non-Rigid Structure-from-Motion (NRSfM) techniques to deal with the exploratory sequences typical of SLAM. A deformation tracking thread recovers the pose of the camera and the deformation of the observed map at frame rate by means of SfT. A deformation mapping thread runs in parallel to update the template at keyframe rate by means of NRSfM with a batch of covisible keyframes. In our experiments, DefSLAM processes sequences of deforming scenes both in a laboratory controlled experiment and in medical endoscopy sequences, being able to produce accurate 3D models of the scene with respect to the moving camera.

Connected through hyperedge HE-1 (induced by source paper "extremely dense point correspondences using a learned feature descriptor"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "an evaluation of feature matchers forfundamental matrix estimation"
    - "3d reconstruction of whole stomach from endoscope video using structure from motion"
    - "mis slam real time large scale dense deformable slam system in minimal invasive surgery based on heterogeneous computing"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "normface l 2 hypersphere embedding for face verification"
    - "focal loss for dense object detection"
    - "working hard to know your neighbor s margins local descriptor learning loss"

Connected through hyperedge HE-2 (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"
    - "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"
    - "sparse then dense alignment based 3d map reconstruction method for endoscopic capsule robots"
    - "unsupervised learning of depth and ego 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)