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: "semantic graph convolutional networks for 3d human pose regression"
  Abstract: In this paper, we study the problem of learning Graph Convolutional Networks (GCNs) for regression. Current architectures of GCNs are limited to the small receptive field of convolution filters and shared transformation matrix for each node. To address these limitations, we propose Semantic Graph Convolutional Networks (SemGCN), a novel neural network architecture that operates on regression tasks with graph-structured data. SemGCN learns to capture semantic information such as local and global node relationships, which is not explicitly represented in the graph. These semantic relationships can be learned through end-to-end training from the ground truth without additional supervision or hand-crafted rules. We further investigate applying SemGCN to 3D human pose regression. Our formulation is intuitive and sufficient since both 2D and 3D human poses can be represented as a structured graph encoding the relationships between joints in the skeleton of a human body. We carry out comprehensive studies to validate our method. The results prove that SemGCN outperforms state of the art while using 90% fewer parameters.

Connected through hyperedge HE-1 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation"):
  with members:
    - "monocular 3d human pose estimation by generation and ordinal ranking"
    - "cross view fusion for 3d human pose estimation"
    - "learnable triangulation of human pose"
    - "distill knowledge from nrsfm for weakly supervised 3d pose learning"
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "billion scale semi supervised learning for image classification"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "texturepose supervising human mesh estimation with texture consistency"

Connected through hyperedge HE-2 (induced by source paper "adargcn adaptive aggregation gcn for few shot learning"):
  with members:
    - "dual graph convolutional network for semantic segmentation"
    - "meta learning with differentiable convex optimization"
    - "infinite mixture prototypes for few shot learning"
    - "t gcn a temporal graph convolutional network for traffic prediction"
    - "edge labeling graph neural network for few shot learning"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "a closer look at few shot classification"
    - "few shot learning with embedded class models and shot free meta training"

Connected through hyperedge HE-3 (induced by source paper "dynamic multiscale graph neural networks for 3d skeleton based human motion prediction"):
  with members:
    - "3d point cloud processing and learning for autonomous driving"
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "learning trajectory dependencies for human motion prediction"
    - "symbiotic graph neural networks for 3d skeleton based human action recognition and motion prediction"
    - "imitation learning for human pose prediction"
    - "human motion prediction via learning local structure representations and temporal dependencies"
    - "spatio temporal manifold learning for human motions via long horizon modeling"
    - "a neural temporal model for human motion prediction"

Connected through hyperedge HE-4 (induced by source paper "back to the future joint aware temporal deep learning 3d human pose estimation"):
  with members:
    - "self supervised learning of 3d human pose using multi view geometry"
    - "deep high resolution representation learning for human pose estimation"
    - "weakly supervised discovery of geometry aware representation for 3d human pose estimation"
    - "modeling human motion with quaternion based neural networks"
    - "ordinal depth supervision for 3d human pose estimation"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "learning 3d human dynamics from video"
    - "2d 3d pose estimation and action recognition using multitask deep learning"

Connected through hyperedge HE-5 (induced by source paper "deep nrsfm towards 3d reconstruction in the wild"):
  with members:
    - "distill knowledge from nrsfm for weakly supervised 3d pose learning"
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "unsupervised 3d pose estimation with geometric self supervision"
    - "deep high resolution representation learning for human pose estimation"
    - "deep non rigid structure from motion"
    - "learning category specific mesh reconstruction from image collections"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"

Connected through hyperedge HE-6 (induced by source paper "learning 3d human shape and pose from dense body parts"):
  with members:
    - "human mesh recovery from monocular images via a skeleton disentangled representation"
    - "texturepose supervising human mesh estimation with texture consistency"
    - "deephuman 3d human reconstruction from a single image"
    - "moulding humans non parametric 3d human shape estimation from single images"
    - "denserac joint 3d pose and shape estimation by dense render and compare"
    - "learning to reconstruct 3d human pose and shape via model fitting in the loop"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "expressive body capture 3d hands face and body from a single image"

Connected through hyperedge HE-7 (induced by source paper "multiview consistent semi supervised learning for 3d human pose estimation"):
  with members:
    - "monocular 3d human pose estimation by generation and ordinal ranking"
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "in the wild human pose estimation using explicit 2d features and intermediate 3d representations"
    - "3d human pose machines with self supervised learning"
    - "learning to reconstruct 3d human pose and shape via model fitting in the loop"
    - "exploiting temporal context for 3d human pose estimation in the wild"
    - "unsupervised 3d pose estimation with geometric self supervision"

Connected through hyperedge HE-8 (induced by source paper "learning feature to feature translator by alternating back propagation for generative zero shot learning"):
  with members:
    - "rethinking zero shot learning a conditional visual classification perspective"
    - "learning where to look semantic guided multi attention localization for zero shot learning"
    - "quantized densely connected u nets for efficient landmark localization"
    - "learning to forecast and refine residual motion for image to video generation"
    - "cu net coupled u nets"
    - "cr gan learning complete representations for multi view generation"
    - "learning descriptor networks for 3d shape synthesis and analysis"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human 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)