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 kinematic chain space for monocular motion capture"
  Abstract: This paper deals with motion capture of kinematic chains (e.g. human skeletons) from monocular image sequences taken by uncalibrated cameras. We present a method based on projecting an observation into a kinematic chain space (KCS). An optimization of the nuclear norm is proposed that implicitly enforces structural properties of the kinematic chain. Unlike other approaches our method does not require specific camera or object motion and is not relying on training data or previously determined constraints such as particular body lengths. The proposed algorithm is able to reconstruct scenes with limited camera motion and previously unseen motions. It is not only applicable to human skeletons but also to other kinematic chains for instance animals or industrial robots. We achieve state-of-the-art results on different benchmark data bases and real world scenes.

Connected through hyperedge HE-1 (induced by source paper "3d hand pose estimation in the wild via graph refinement under adversarial learning"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "3d hand shape and pose from images in the wild"
    - "pushing the envelope for rgb based dense 3d hand pose estimation via neural rendering"
    - "single image 3d hand reconstruction with mesh convolutions"
    - "deepgcns can gcns go as deep as cnns"
    - "disentangling latent hands for image synthesis and pose estimation"
    - "3d human pose estimation in the wild by adversarial learning"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "structure from articulated motion accurate and stable monocular 3d reconstruction without training data"):
  with members:
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "in the wild human pose estimation using explicit 2d features and intermediate 3d representations"
    - "semantic graph convolutional networks for 3d human pose regression"
    - "exploiting temporal context for 3d human pose estimation in the wild"
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "ordinal depth supervision for 3d human pose estimation"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"

Connected through hyperedge HE-3 (induced by source paper "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"):
  with members:
    - "learning to estimate 3d human pose and shape from a single color image"
    - "3d human pose estimation in the wild by adversarial learning"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "orinet a fully convolutional network for 3d human pose estimation"
    - "exploiting temporal information for 3d human pose estimation"
    - "adversarial inverse graphics networks learning 2d to 3d lifting and image to image translation from unpaired supervision"
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"

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)