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: "the 2017 hands in the million challenge on 3d hand pose estimation"
  Abstract: We present the 2017 Hands in the Million Challenge, a public competition designed for the evaluation of the task of 3D hand pose estimation. The goal of this challenge is to assess how far is the state of the art in terms of solving the problem of 3D hand pose estimation as well as detect major failure and strength modes of both systems and evaluation metrics that can help to identify future research directions. The challenge follows up the recent publication of BigHand2.2M and First-Person Hand Action datasets, which have been designed to exhaustively cover multiple hand, viewpoint, hand articulation, and occlusion. The challenge consists of a standardized dataset, an evaluation protocol for two different tasks, and a public competition. In this document we describe the different aspects of the challenge and, jointly with the results of the participants, it will be presented at the 3rd International Workshop on Observing and Understanding Hands in Action, HANDS 2017, with ICCV 2017.

Connected through hyperedge HE-1 (induced by source paper "a2j anchor to joint regression network for 3d articulated pose estimation from a single depth image"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "hand3d hand pose estimation using 3d neural network"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "deepprior improving fast and accurate 3d hand pose estimation"

Connected through hyperedge HE-2 (induced by source paper "silhouette net 3d hand pose estimation from silhouettes"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "cross modal deep variational hand pose estimation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "hand3d hand pose estimation using 3d neural network"
    - "deepprior improving fast and accurate 3d hand pose estimation"
    - "towards good practices for deep 3d hand pose estimation"

Connected through hyperedge HE-3 (induced by source paper "hand range of motion evaluation for rheumatoid arthritis patients"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "back to rgb 3d tracking of hands and hand object interactions based on short baseline stereo"
    - "using a single rgb frame for real time 3d hand pose estimation in the wild"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"
    - "deepprior improving fast and accurate 3d hand pose estimation"

Connected through hyperedge HE-4 (induced by source paper "point to pose voting based hand pose estimation using residual permutation equivariant layer"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "deep sets"
    - "so net self organizing network for point cloud analysis"
    - "dynamic graph cnn for learning on point clouds"
    - "triplet center loss for multi view 3d object retrieval"
    - "occlusion aware hand pose estimation using hierarchical mixture density network"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "v2v posenet voxel to voxel prediction network for accurate 3d hand and human pose estimation from a single depth map"

Connected through hyperedge HE-5 (induced by source paper "model based hand pose estimation for generalized hand shape with appearance normalization"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "occlusion aware hand pose estimation using hierarchical mixture density network"
    - "learning to estimate 3d hand pose from single rgb images"
    - "v2v posenet voxel to voxel prediction network for accurate 3d hand and human pose estimation from a single depth map"
    - "hand3d hand pose estimation using 3d neural network"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"
    - "deepprior improving fast and accurate 3d hand pose estimation"

Connected through hyperedge HE-6 (induced by source paper "v2v posenet voxel to voxel prediction network for accurate 3d hand and human pose estimation from a single depth map"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "hand3d hand pose estimation using 3d neural network"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"
    - "deepprior improving fast and accurate 3d hand pose estimation"
    - "towards good practices for deep 3d hand pose estimation"
    - "end to end global to local cnn learning for hand pose recovery in depth data"

Connected through hyperedge HE-7 (induced by source paper "depth based 3d hand pose estimation from current achievements to future goals"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "hand3d hand pose estimation using 3d neural network"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"
    - "deepprior improving fast and accurate 3d hand pose estimation"
    - "compositional human pose regression"

Connected through hyperedge HE-8 (induced by source paper "first person hand action benchmark with rgb d videos and 3d hand pose annotations"):
  with members:
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"
    - "real time hand tracking under occlusion from an egocentric rgb d sensor"
    - "depth based 3d hand pose estimation from current achievements to future goals"
    - "sembed semantic embedding of egocentric action videos"
    - "convolutional pose machines"
    - "training a feedback loop for hand pose estimation"

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)