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: "3d human pose machines with self supervised learning"
  Abstract: Driven by recent computer vision and robotic applications, recovering 3D human poses has become increasingly important and attracted growing interests. In fact, completing this task is quite challenging due to the diverse appearances, viewpoints, occlusions and inherently geometric ambiguities inside monocular images. Most of the existing methods focus on designing some elaborate priors /constraints to directly regress 3D human poses based on the corresponding 2D human pose-aware features or 2D pose predictions. However, due to the insufficient 3D pose data for training and the domain gap between 2D space and 3D space, these methods have limited scalabilities for all practical scenarios (e.g., outdoor scene). Attempt to address this issue, this paper proposes a simple yet effective self-supervised correction mechanism to learn all intrinsic structures of human poses from abundant images. Specifically, the proposed mechanism involves two dual learning tasks, i.e., the 2D-to-3D pose transformation and 3D-to-2D pose projection, to serve as a bridge between 3D and 2D human poses in a type of "free" self-supervision for accurate 3D human pose estimation. The 2D-to-3D pose implies to sequentially regress intermediate 3D poses by transforming the pose representation from the 2D domain to the 3D domain under the sequence-dependent temporal context, while the 3D-to-2D pose projection contributes to refining the intermediate 3D poses by maintaining geometric consistency between the 2D projections of 3D poses and the estimated 2D poses. We further apply our self-supervised correction mechanism to develop a 3D human pose machine, which jointly integrates the 2D spatial relationship, temporal smoothness of predictions and 3D geometric knowledge. Extensive evaluations demonstrate the superior performance and efficiency of our framework over all the compared competing methods.

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"
    - "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"
    - "unsupervised 3d pose estimation with geometric self supervision"

Connected through hyperedge HE-2 (induced by source paper "recent advances in imitation learning from observation"):
  with members:
    - "hybrid reinforcement learning with expert state sequences"
    - "sample efficient adversarial imitation learning from observation"
    - "imitation learning from video by leveraging proprioception"
    - "ridm reinforced inverse dynamics modeling for learning from a single observed demonstration"
    - "imitating latent policies from observation"
    - "internal model from observations for reward shaping"
    - "an algorithmic perspective on imitation learning"
    - "one shot learning of multi step tasks from observation via activity localization in auxiliary video"

Connected through hyperedge HE-3 (induced by source paper "joint representation of multiple geometric priors via a shape decomposition model for single monocular 3d pose estimation"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "monocap monocular human motion capture using a cnn coupled with a geometric prior"
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "learning from synthetic humans"
    - "recurrent 3d pose sequence machines"

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)