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: "learning camera viewpoint using cnn to improve 3d body pose estimation"
  Abstract: The objective of this work is to estimate 3D human pose from a single RGB image. Extracting image representations which incorporate both spatial relation of body parts and their relative depth plays an essential role in accurate3D pose reconstruction. In this paper, for the first time, we show that camera viewpoint in combination to 2D joint lo-cations significantly improves 3D pose accuracy without the explicit use of perspective geometry mathematical models.To this end, we train a deep Convolutional Neural Net-work (CNN) to learn categorical camera viewpoint. To make the network robust against clothing and body shape of the subject in the image, we utilized 3D computer rendering to synthesize additional training images. We test our framework on the largest 3D pose estimation bench-mark, Human3.6m, and achieve up to 20% error reduction compared to the state-of-the-art approaches that do not use body part segmentation.

Connected through hyperedge HE-1 (induced by source paper "learning multi human optical flow"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "learning human optical flow"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "learning from synthetic humans"
    - "mask r cnn"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

Connected through hyperedge HE-2 (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"
    - "exploiting temporal context for 3d human pose estimation in the wild"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "synthetic humans for action recognition from unseen viewpoints"):
  with members:
    - "temporally coherent full 3d mesh human pose recovery from monocular video"
    - "hierarchically learned view invariant representations for cross view action recognition"
    - "learning joint reconstruction of hands and manipulated objects"
    - "learning to train with synthetic humans"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "sim2real transfer learning for 3d pose estimation motion to the rescue"
    - "virtualhome simulating household activities via programs"
    - "human action recognition and prediction a survey"

Connected through hyperedge HE-4 (induced by source paper "learning to train with synthetic humans"):
  with members:
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "learning human optical flow"
    - "improving spatiotemporal self supervision by deep reinforcement learning"
    - "image based synthesis for deep 3d human pose estimation"
    - "learning to refine human pose estimation"
    - "a closed form solution to photorealistic image stylization"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "how robust is 3d human pose estimation to occlusion"

Connected through hyperedge HE-5 (induced by source paper "feature boosting network for 3d pose estimation"):
  with members:
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "monocap monocular human motion capture using a cnn coupled with a geometric prior"
    - "learning to estimate 3d hand pose from single rgb images"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "compositional human pose regression"
    - "ganerated hands for real time 3d hand tracking from monocular rgb"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-6 (induced by source paper "view invariant 3d human pose estimation"):
  with members:
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "3d human pose estimation in the wild by adversarial learning"
    - "exploiting temporal information for 3d human pose estimation"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "self adversarial training for human pose estimation"
    - "end to end recovery of human shape and pose"
    - "compositional human pose regression"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"

Connected through hyperedge HE-7 (induced by source paper "improving face anti spoofing by 3d virtual synthesis"):
  with members:
    - "face alignment in full pose range a 3d total solution"
    - "face synthesis for eyeglass robust face recognition"
    - "exploiting temporal and depth information for multi frame face anti spoofing"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "on the learning of deep local features for robust face spoofing detection"
    - "a performance evaluation of convolutional neural networks for face anti spoofing"
    - "mocap guided data augmentation for 3d pose estimation in the wild"
    - "synthesizing training images for boosting human 3d pose estimation"

Connected through hyperedge HE-8 (induced by source paper "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"):
  with members:
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "domain adaptation for visual applications a comprehensive survey"
    - "robust imitation of diverse behaviors"
    - "learning from synthetic humans"
    - "emergence of locomotion behaviours in rich environments"
    - "how useful is photo realistic rendering for visual learning"
    - "unrealcv connecting computer vision to unreal engine"
    - "semantic scene completion from a single depth image"

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)