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: "depth based 3d hand pose estimation from current achievements to future goals"
  Abstract: In this paper, we strive to answer two questions: What is the current state of 3D hand pose estimation from depth images? And, what are the next challenges that need to be tackled? Following the successful Hands In the Million Challenge (HIM2017), we investigate the top 10 state-of-the-art methods on three tasks: single frame 3D pose estimation, 3D hand tracking, and hand pose estimation during object interaction. We analyze the performance of different CNN structures with regard to hand shape, joint visibility, view point and articulation distributions. Our findings include: (1) isolated 3D hand pose estimation achieves low mean errors (10 mm) in the view point range of [70, 120] degrees, but it is far from being solved for extreme view points; (2) 3D volumetric representations outperform 2D CNNs, better capturing the spatial structure of the depth data; (3) Discriminative methods still generalize poorly to unseen hand shapes; (4) While joint occlusions pose a challenge for most methods, explicit modeling of structure constraints can significantly narrow the gap between errors on visible and occluded joints.

Connected through hyperedge HE-1 (induced by source paper "rotation invariant mixed graphical model network for 2d hand pose estimation"):
  with members:
    - "adaptive graphical model network for 2d handpose estimation"
    - "3d hand shape and pose estimation from a single rgb image"
    - "geometric pose affordance 3d human pose with scene constraints"
    - "3d hand shape and pose from images in the wild"
    - "deep high resolution representation learning for human pose estimation"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "multi scale structure aware network for human pose estimation"
    - "augmented skeleton space transfer for depth based hand pose estimation"

Connected through hyperedge HE-2 (induced by source paper "nonparametric structure regularization machine for 2d hand pose estimation"):
  with members:
    - "adaptive graphical model network for 2d handpose estimation"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "multi scale structure aware network for human pose estimation"
    - "exploiting temporal information for 3d human pose estimation"
    - "thin slicing network a deep structured model for pose estimation in videos"
    - "learning to estimate 3d hand pose from single rgb images"
    - "using a single rgb frame for real time 3d hand pose estimation in the wild"

Connected through hyperedge HE-3 (induced by source paper "attention a lightweight 2d hand pose estimation approach"):
  with members:
    - "real time facial surface geometry from monocular video on mobile gpus"
    - "making convolutional networks shift invariant again"
    - "freihand a dataset for markerless capture of hand pose and shape from single rgb images"
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "3d hand shape and pose from images in the wild"
    - "attention augmented convolutional networks"
    - "mish a self regularized non monotonic neural activation function"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-4 (induced by source paper "fast and accurate 3d hand pose estimation via recurrent neural network for capturing hand articulations"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "a2j anchor to joint regression network for 3d articulated pose estimation from a single depth image"
    - "model based hand pose estimation for generalized hand shape with appearance normalization"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "deepprior improving fast and accurate 3d hand pose estimation"
    - "end to end global to local cnn learning for hand pose recovery in depth data"
    - "dynamics based 3d skeletal hand tracking"

Connected through hyperedge HE-5 (induced by source paper "end to end hand mesh recovery from a monocular rgb image"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "3d hand shape and pose from images in the wild"
    - "face alignment in full pose range a 3d total solution"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"

Connected through hyperedge HE-6 (induced by source paper "pushing the envelope for rgb based dense 3d hand pose estimation via neural rendering"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "analysis of hand segmentation in the wild"
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "deephps end to end estimation of 3d hand pose and shape by learning from synthetic depth"
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "cross modal deep variational hand pose estimation"

Connected through hyperedge HE-7 (induced by source paper "3d hand shape and pose estimation from a single rgb image"):
  with members:
    - "3d hand shape and pose from images in the wild"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "domain transfer for 3d pose estimation from color images without manual annotations"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "deephps end to end estimation of 3d hand pose and shape by learning from synthetic depth"

Connected through hyperedge HE-8 (induced by source paper "h o unified egocentric recognition of 3d hand object poses and interactions"):
  with members:
    - "learning joint reconstruction of hands and manipulated objects"
    - "scaling egocentric vision the epic kitchens dataset"
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "cross modal deep variational hand pose estimation"
    - "object level visual reasoning in videos"
    - "occlusion aware hand pose estimation using hierarchical mixture density network"
    - "detecting and recognizing human object interactions"
    - "learning to estimate 3d hand pose from single rgb images"

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)