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 "end to end recovery of human shape and pose"):
  Abstract: We describe Human Mesh Recovery (HMR), an end-to-end framework for reconstructing a full 3D mesh of a human body from a single RGB image. In contrast to most current methods that compute 2D or 3D joint locations, we produce a richer and more useful mesh representation that is parameterized by shape and 3D joint angles. The main objective is to minimize the reprojection loss of keypoints, which allow our model to be trained using in-the-wild images that only have ground truth 2D annotations. However, reprojection loss alone is highly under constrained. In this work we address this problem by introducing an adversary trained to tell whether a human body parameter is real or not using a large database of 3D human meshes. We show that HMR can be trained with and without using any coupled 2D-to-3D supervision. We do not rely on intermediate 2D keypoint detection and infer 3D pose and shape parameters directly from image pixels. Our model runs in real-time given a bounding box containing the person. We demonstrate our approach on various images in-the-wild and out-perform previous optimizationbased methods that output 3D meshes and show competitive results on tasks such as 3D joint location estimation and part segmentation.

Includes members:
  Member "densereg fully convolutional dense shape regression in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning nonparametric human mesh reconstruction from a single image without ground truth meshes")
  Member "adversarial inverse graphics networks learning 2d to 3d lifting and image to image translation from unpaired supervision":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "sparse representation for 3d shape estimation a convex relaxation approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ego pose estimation and forecasting as real time pd control")
  Member "lifting from the deep convolutional 3d pose estimation from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weakly supervised 3d human pose learning via multi view images in the wild")
  Member "self supervised learning of motion capture":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "compositional human pose regression":
    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 "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "unite the people closing the loop between 3d and 2d human representations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")

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)