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 "camera distance aware top down approach for 3d multi person pose estimation from a single rgb image"):
  Abstract: Although significant improvement has been achieved recently in 3D human pose estimation, most of the previous methods only treat a single-person case. In this work, we firstly propose a fully learning-based, camera distance-aware top-down approach for 3D multi-person pose estimation from a single RGB image. The pipeline of the proposed system consists of human detection, absolute 3D human root localization, and root-relative 3D single-person pose estimation modules. Our system achieves comparable results with the state-of-the-art 3D single-person pose estimation models without any groundtruth information and significantly outperforms previous 3D multi-person pose estimation methods on publicly available datasets. #R##N#The code is available in this https URL , this https URL.

Includes members:
  Member "multi scale aggregation r cnn for 2d multi person pose estimation":
    also connected through other hyperedges:
      (none)
  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 "deep attention based classification network for robust depth prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "from open set to closed set supervised spatial divide and conquer for object counting")
  Member "posefix model agnostic general human pose refinement network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "patch based 3d human pose refinement")
  Member "deep ordinal regression network for monocular depth estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "total capture a 3d deformation model for tracking faces hands and bodies":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deepcap monocular human performance capture using weak supervision")
  Member "simple baselines for human pose estimation and tracking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "3d human pose estimation in the wild by adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "posenet3d unsupervised 3d human shape and 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)