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 "integral human pose regression"):
  Abstract: State-of-the-art human pose estimation methods are dominated by complex multi-stage architectures using a heat map representation. The representation has a few issues in nature, such as no end-to-end training, quantization error and sensitivity to multiple modes. While pose estimation is essentially a regression problem, existing regression approaches are not as effective. This work proposes an integral regression approach that unifies the heat map representation and joint regression, thus possessing the merits of both paradigms. It is differentiable and allows end-to-end training. It is simple, fast, and compatible with any heat map based methods. Comprehensive experiments verify its efficacy. Building upon simple baseline networks, its performance on benchmarks is strong and competitive to state-of-the-art.

Includes members:
  Member "exploiting temporal information for 3d human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "numerical coordinate regression with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "metric scale truncation robust heatmaps for 3d human pose estimation")
  Member "monocap monocular human motion capture using a cnn coupled with a geometric prior":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unipose unified human pose estimation in single images and videos")
  Member "learning pose grammar to encode human body configuration for 3d pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hierarchical human parsing with typed part relation reasoning")
  Member "towards 3d human pose estimation in the wild a weakly supervised approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "weakly supervised 3d human pose learning via multi view images in the wild")
  Member "lifting from the deep convolutional 3d pose estimation from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weakly supervised 3d human pose learning via multi view images in the wild")
  Member "learning feature pyramids for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deformation aware unpaired image translation for pose estimation on laboratory animals")
  Member "self adversarial training for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "augmented parallel pyramid net for attention guided pose estimation")

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)