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: "exploiting temporal context for 3d human pose estimation in the wild"
  Abstract: We present a bundle-adjustment-based algorithm for recovering accurate 3D human pose and meshes from monocular videos. Unlike previous algorithms which operate on single frames, we show that reconstructing a person over an entire sequence gives extra constraints that can resolve ambiguities. This is because videos often give multiple views of a person, yet the overall body shape does not change and 3D positions vary slowly. Our method improves not only on standard mocap-based datasets like Human 3.6M -- where we show quantitative improvements -- but also on challenging in-the-wild datasets such as Kinetics. Building upon our algorithm, we present a new dataset of more than 3 million frames of YouTube videos from Kinetics with automatically generated 3D poses and meshes. We show that retraining a single-frame 3D pose estimator on this data improves accuracy on both real-world and mocap data by evaluating on the 3DPW and HumanEVA datasets.

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 "weakly supervised 3d human pose learning via multi view images 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"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "texturepose supervising human mesh estimation with texture consistency"
    - "in the wild human pose estimation using explicit 2d features and intermediate 3d representations"
    - "learning the depths of moving people by watching frozen people"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "learning to reconstruct 3d human pose and shape via model fitting in the loop"

Connected through hyperedge HE-3 (induced by source paper "hierarchical kinematic human mesh recovery"):
  with members:
    - "repose learning deep kinematic priors for fast human pose estimation"
    - "texturepose supervising human mesh estimation with texture consistency"
    - "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"
    - "densebody directly regressing dense 3d human pose and shape from a single color image"
    - "vibe video inference for human body pose and shape estimation"

Connected through hyperedge HE-4 (induced by source paper "sim2real transfer learning for 3d pose estimation motion to the rescue"):
  with members:
    - "does computer vision matter for action"
    - "learning the depths of moving people by watching frozen people"
    - "an annotation saved is an annotation earned using fully synthetic training for object instance detection"
    - "ordinal depth supervision for 3d human pose estimation"
    - "learning human optical flow"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "learning 3d human dynamics from video"

Connected through hyperedge HE-5 (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"
    - "semantic graph convolutional networks for 3d human pose regression"

Connected through hyperedge HE-6 (induced by source paper "vibe video inference for human body pose and shape estimation"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "structured prediction helps 3d human motion modelling"
    - "xnect real time multi person 3d human pose estimation with a single rgb camera"
    - "learning to reconstruct 3d human pose and shape via model fitting in the loop"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "from variational to deterministic autoencoders"
    - "expressive body capture 3d hands face and body from a single image"

Connected through hyperedge HE-7 (induced by source paper "texturepose supervising human mesh estimation with texture consistency"):
  with members:
    - "self supervised learning of 3d human pose using multi view geometry"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "in the wild human pose estimation using explicit 2d features and intermediate 3d representations"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "expressive body capture 3d hands face and body from a single image"
    - "ordinal depth supervision for 3d human pose estimation"
    - "learning human optical flow"
    - "learning category specific mesh reconstruction from image collections"

Connected through hyperedge HE-8 (induced by source paper "predicting 3d human dynamics from video"):
  with members:
    - "xnect real time multi person 3d human pose estimation with a single rgb camera"
    - "sim2real transfer learning for 3d pose estimation motion to the rescue"
    - "deep lip reading a comparison of models and an online application"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "learning 3d human dynamics from video"
    - "flow grounded spatial temporal video prediction from still 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)