human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "synthetic humans for action recognition from unseen viewpoints"):
  Abstract: Our goal in this work is to improve the performance of human action recognition for viewpoints unseen during training by using synthetic training data. Although synthetic data has been shown to be beneficial for tasks such as human pose estimation, its use for RGB human action recognition is relatively unexplored. We make use of the recent advances in monocular 3D human body reconstruction from real action sequences to automatically render synthetic training videos for the action labels. We make the following contributions: (i) we investigate the extent of variations and augmentations that are beneficial to improving performance at new viewpoints. We consider changes in body shape and clothing for individuals, as well as more action relevant augmentations such as non-uniform frame sampling, and interpolating between the motion of individuals performing the same action; (ii) We introduce a new dataset, SURREACT, that allows supervised training of spatio-temporal CNNs for action classification; (iii) We substantially improve the state-of-the-art action recognition performance on the NTU RGB+D and UESTC standard human action multi-view benchmarks; Finally, (iv) we extend the augmentation approach to in-the-wild videos from a subset of the Kinetics dataset to investigate the case when only one-shot training data is available, and demonstrate improvements in this case as well.

Includes members:
  Member "temporally coherent full 3d mesh human pose recovery from monocular video":
    also connected through other hyperedges:
      (none)
  Member "hierarchically learned view invariant representations for cross view action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep image to video adaptation and fusion networks for action recognition")
  Member "learning joint reconstruction of hands and manipulated objects":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust occlusion aware pose estimation for objects grasped by adaptive hands")
  Member "learning to train with synthetic humans":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to dress 3d people in generative clothing")
  Member "convolutional mesh regression for single image human shape reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "sim2real transfer learning for 3d pose estimation motion to the rescue":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "vibe video inference for human body pose and shape estimation")
  Member "virtualhome simulating household activities via programs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "arena a general evaluation platform and building toolkit for multi agent intelligence")
  Member "human action recognition and prediction a survey":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "action segmentation with joint self supervised temporal domain adaptation")

Question: Please tell me which class the source paper 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)