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 "learnable triangulation of human pose"):
  Abstract: We present two novel solutions for multi-view 3D human pose estimation based on new learnable triangulation methods that combine 3D information from multiple 2D views. The first (baseline) solution is a basic differentiable algebraic triangulation with an addition of confidence weights estimated from the input images. The second solution is based on a novel method of volumetric aggregation from intermediate 2D backbone feature maps. The aggregated volume is then refined via 3D convolutions that produce final 3D joint heatmaps and allow modelling a human pose prior. Crucially, both approaches are end-to-end differentiable, which allows us to directly optimize the target metric. We demonstrate transferability of the solutions across datasets and considerably improve the multi-view state of the art on the Human3.6M dataset. Video demonstration, annotations and additional materials will be posted on our project page (this https URL).

Includes members:
  Member "self supervised learning of 3d human pose using multi view geometry":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "rethinking pose in 3d multi stage refinement and recovery for markerless motion capture":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cross view tracking for multi human 3d pose estimation at over 100 fps")
  Member "a generalizable approach for multi view 3d human pose regression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deepfuse an imu aware network for real time 3d human pose estimation from multi view image")
  Member "humbi 1 0 human multiview behavioral imaging dataset":
    also connected through other hyperedges:
      (none)
  Member "3d human pose estimation in video with temporal convolutions and semi supervised training":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "simple baselines for human pose estimation and tracking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "monocular total capture posing face body and hands in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deepcap monocular human performance capture using weak supervision")
  Member "learning spatial common sense with geometry aware recurrent networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning from unlabelled videos using contrastive predictive neural 3d mapping")

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)