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 "neural body fitting unifying deep learning and model based human pose and shape estimation"):
  Abstract: Direct prediction of 3D body pose and shape remains a challenge even for highly parameterized deep learning models. Mapping from the 2D image space to the prediction space is difficult: perspective ambiguities make the loss function noisy and training data is scarce. In this paper, we propose a novel approach (Neural Body Fitting (NBF)). It integrates a statistical body model within a CNN, leveraging reliable bottom-up semantic body part segmentation and robust top-down body model constraints. NBF is fully differentiable and can be trained using 2D and 3D annotations. In detailed experiments, we analyze how the components of our model affect performance, especially the use of part segmentations as an explicit intermediate representation, and present a robust, efficiently trainable framework for 3D human pose estimation from 2D images with competitive results on standard benchmarks. Code will be made available at this http URL

Includes members:
  Member "lcr net multi person 2d and 3d pose detection in natural images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "from kinematics to dynamics estimating center of pressure and base of support from video frames of human motion")
  Member "ordinal depth supervision for 3d human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weakly supervised 3d human pose learning via multi view images in the wild")
  Member "learning to estimate 3d human pose and shape from a single color image":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "bodynet volumetric inference of 3d human body shapes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "video based reconstruction of 3d people models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "detailed human avatars from monocular video":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "densepose dense human pose estimation in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation")
  Member "monocap monocular human motion capture using a cnn coupled with a geometric prior":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unipose unified human pose estimation in single images and videos")

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)