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: "pose embeddings a deep architecture for learning to match human poses"
  Abstract: We present a method for learning an embedding that places images of humans in similar poses nearby. This embedding can be used as a direct method of comparing images based on human pose, avoiding potential challenges of estimating body joint positions. Pose embedding learning is formulated under a triplet-based distance criterion. A deep architecture is used to allow learning of a representation capable of making distinctions between different poses. Experiments on human pose matching and retrieval from video data demonstrate the potential of the method.

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"
    - "self supervised learning of pose embeddings from spatiotemporal relations in videos"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "siamese instance search for tracking"
    - "spice semantic propositional image caption evaluation"

Connected through hyperedge HE-3 (induced by source paper "view invariant probabilistic embedding for human pose"):
  with members:
    - "cross view fusion for 3d human pose estimation"
    - "learnable triangulation of human pose"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "unsupervised 3d pose estimation with geometric self supervision"
    - "vehicle re identification with viewpoint aware metric learning"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "unsupervised geometry aware representation for 3d human pose estimation"

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 "time contrastive networks self supervised learning from video"):
  with members:
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "third person imitation learning"
    - "one shot imitation learning"
    - "lift learned invariant feature transform"
    - "generic 3d representation via pose estimation and matching"
    - "understanding visual concepts with continuation learning"
    - "learning features by watching objects move"

Connected through hyperedge HE-6 (induced by source paper "self supervised learning of pose embeddings from spatiotemporal relations in videos"):
  with members:
    - "stacked hourglass networks for human pose estimation"
    - "generic 3d representation via pose estimation and matching"
    - "shuffle and learn unsupervised learning using temporal order verification"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "structured feature learning for pose estimation"
    - "colorful image colorization"
    - "unsupervised visual representation learning by context prediction"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

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)