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 "curriculum deepsdf"):
  Abstract: When learning to sketch, beginners start with simple and flexible shapes, and then gradually strive for more complex and accurate ones in the subsequent training sessions. In this paper, we design a "shape curriculum" for learning continuous Signed Distance Function (SDF) on shapes, namely Curriculum DeepSDF. Inspired by how humans learn, Curriculum DeepSDF organizes the learning task in ascending order of difficulty according to the following two criteria: surface accuracy and sample difficulty. The former considers stringency in supervising with ground truth, while the latter regards the weights of hard training samples near complex geometry and fine structure. More specifically, Curriculum DeepSDF learns to reconstruct coarse shapes at first, and then gradually increases the accuracy and focuses more on complex local details. Experimental results show that a carefully-designed curriculum leads to significantly better shape reconstructions with the same training data, training epochs and network architecture as DeepSDF. We believe that the application of shape curricula can benefit the training process of a wide variety of 3D shape representation learning methods.

Includes members:
  Member "implicit geometric regularization for learning shapes":
    also connected through other hyperedges:
      (none)
  Member "learning shape templates with structured implicit functions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "deepsdf learning continuous signed distance functions for shape representation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "on the power of curriculum learning in training deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "curriculum labeling self paced pseudo labeling for semi supervised learning")
  Member "deep level sets implicit surface representations for 3d shape inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "learning to infer implicit surfaces without 3d supervision":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "deep structured implicit functions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "disn deep implicit surface network for high quality single view 3d reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on deep geometry learning from a representation perspective")

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)