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 "honnotate a method for 3d annotation of hand and objects poses"):
  Abstract: We propose a method for annotating images of a hand manipulating an object with the 3D poses of both the hand and the object, together with a dataset created using this method. There is a current lack of annotated real images for this problem, as estimating the 3D poses is challenging, mostly because of the mutual occlusions between the hand and the object. To tackle this challenge, we capture sequences with one or several RGB-D cameras, and jointly optimizes the 3D hand and object poses over all the frames simultaneously. This method allows us to automatically annotate each frame with accurate estimates of the poses, despite large mutual occlusions. With this method, we created HO-3D, the first markerless dataset of color images with 3D annotations of both hand and object. This dataset is currently made of 80,000 frames, 65 sequences, 10 persons, and 10 objects, and growing. We also use it to train a deepnet to perform RGB-based single frame hand pose estimation and provide a baseline on our dataset.

Includes members:
  Member "freihand a dataset for markerless capture of hand pose and shape from single rgb images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "attention a lightweight 2d hand pose estimation approach")
  Member "learning single image 3d reconstruction by generative modelling of shape pose and shading":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "inverse graphics gan learning to generate 3d shapes from unstructured 2d data")
  Member "learning to estimate pose and shape of hand held objects from rgb images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust occlusion aware pose estimation for objects grasped by adaptive hands")
  Member "learning joint reconstruction of hands and manipulated objects":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust occlusion aware pose estimation for objects grasped by adaptive hands")
  Member "h o unified egocentric recognition of 3d hand object poses and interactions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hand priming in object localization for assistive egocentric vision")
  Member "implicit 3d orientation learning for 6d object detection from rgb images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cps class level 6d pose and shape estimation from monocular images")
  Member "domain transfer for 3d pose estimation from color images without manual annotations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "3d hand shape and pose estimation from a single rgb image")
  Member "total capture a 3d deformation model for tracking faces hands and bodies":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deepcap monocular human performance capture using weak supervision")

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)