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 "3d menagerie modeling the 3d shape and pose of animals"):
  Abstract: There has been significant prior work on learning realistic, articulated, 3D statistical shape models of the human body. In contrast, there are few such models for animals, despite their many applications. The main challenge is that animals are much less cooperative subjects than humans. The best human body models are learned from thousands of 3D scans of people in specific poses, which is infeasible with live animals. Consequently, here we extend a state-of-the-art articulated 3D human body model to animals and learn it from a limited set of 3D scans of toy figurines in arbitrary poses. We employ a novel part-based shape model to compute an initial registration to the scans. We then normalize their pose, learn a statistical shape model, and refine the alignments and the model together. In this way, we accurately align animal scans from different quadruped families with very different shapes and poses. With the alignment to a common template we learn a shape space representing animals including lions, cats, dogs, horses, cows and hippos. Animal shapes can be sampled from the model, posed, animated, and fitted to data. In particular, we demonstrate the generalization of the model by fitting it to images of real animals, and show that it captures realistic animal shapes, even for new species not seen in training. We make our model available for research, enabling the extension of methods for human shape and pose estimation to animals.

Includes members:
  Member "unite the people closing the loop between 3d and 2d human representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "behavior discovery and alignment of articulated object classes from unstructured video":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "self supervised learning of class embeddings from video")
  Member "keep it smpl automatic estimation of 3d human pose and shape from a single 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 "learning 3d deformation of animals from 2d images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "3dn 3d deformation network")
  Member "category specific object reconstruction from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reactive navigation in partially familiar planar environments using semantic perceptual feedback")

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)