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: "self supervised learning of pose embeddings from spatiotemporal relations in videos"
  Abstract: Human pose analysis is presently dominated by deep convolutional networks trained with extensive manual annotations of joint locations and beyond. To avoid the need for expensive labeling, we exploit spatiotemporal relations in training videos for self-supervised learning of pose embeddings. The key idea is to combine temporal ordering and spatial placement estimation as auxiliary tasks for learning pose similarities in a Siamese convolutional network. Since the self-supervised sampling of both tasks from natural videos can result in ambiguous and incorrect training labels, our method employs a curriculum learning idea that starts training with the most reliable data samples and gradually increases the difficulty. To further refine the training process we mine repetitive poses in individual videos which provide reliable labels while removing inconsistencies. Our pose embeddings capture visual characteristics of human pose that can boost existing supervised representations in human pose estimation and retrieval. We report quantitative and qualitative results on these tasks in Olympic Sports, Leeds Pose Sports and MPII Human Pose datasets.

Connected through hyperedge HE-1 (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"
    - "semantic graph convolutional networks for 3d human pose regression"
    - "exploiting temporal context for 3d human pose estimation in the wild"

Connected through hyperedge HE-2 (induced by source paper "deep metric learning beyond binary supervision"):
  with members:
    - "mining on manifolds metric learning without labels"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"
    - "contrastive learning for image captioning"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "siamese instance search for tracking"
    - "spice semantic propositional image caption evaluation"
    - "deep metric learning via facility location"

Connected through hyperedge HE-3 (induced by source paper "human mesh recovery from monocular images via a skeleton disentangled representation"):
  with members:
    - "learning to estimate 3d human pose and shape from a single color image"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "learning 3d human dynamics from video"
    - "sfv reinforcement learning of physical skills from videos"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "skeleton transformer networks 3d human pose and skinned mesh from single rgb image"
    - "exploiting temporal information for 3d human pose estimation"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"

Connected through hyperedge HE-4 (induced by source paper "3d human pose estimation under limited supervision using metric learning"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "exploring disentangled feature representation beyond face identification"
    - "drpose3d depth ranking in 3d human pose estimation"
    - "learning monocular 3d human pose estimation from multi view images"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "harvesting multiple views for marker less 3d human pose annotations"

Connected through hyperedge HE-5 (induced by source paper "video jigsaw unsupervised learning of spatiotemporal context for video action recognition"):
  with members:
    - "transitive invariance for self supervised visual representation learning"
    - "what actions are needed for understanding human actions in videos"
    - "unsupervised video understanding by reconciliation of posture similarities"
    - "multi task self supervised visual learning"
    - "the kinetics human action video dataset"
    - "unsupervised representation learning by sorting sequences"
    - "cross domain self supervised multi task feature learning using synthetic imagery"
    - "representation learning by learning to count"

Connected through hyperedge HE-6 (induced by source paper "improving spatiotemporal self supervision by deep reinforcement learning"):
  with members:
    - "boosting self supervised learning via knowledge transfer"
    - "unsupervised representation learning by predicting image rotations"
    - "deep unsupervised learning of visual similarities"
    - "deeppermnet visual permutation learning"
    - "transitive invariance for self supervised visual representation learning"
    - "self supervised learning of visual features through embedding images into text topic spaces"
    - "unsupervised video understanding by reconciliation of posture similarities"
    - "unsupervised representation learning by sorting sequences"

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)