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 "pt2pc learning to generate 3d point cloud shapes from part tree conditions"):
  Abstract: 3D generative shape modeling is a fundamental research area in computer vision and interactive computer graphics, with many real-world applications. This paper investigates the novel problem of generating 3D shape point cloud geometry from a symbolic part tree representation. In order to learn such a conditional shape generation procedure in an end-to-end fashion, we propose a conditional GAN "part tree"-to-"point cloud" model (PT2PC) that disentangles the structural and geometric factors. The proposed model incorporates the part tree condition into the architecture design by passing messages top-down and bottom-up along the part tree hierarchy. Experimental results and user study demonstrate the strengths of our method in generating perceptually plausible and diverse 3D point clouds, given the part tree condition. We also propose a novel structural measure for evaluating if the generated shape point clouds satisfy the part tree conditions.

Includes members:
  Member "learning shape templates with structured implicit functions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "structurenet hierarchical graph networks for 3d shape generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "pointflow 3d point cloud generation with continuous normalizing flows":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to segment 3d point clouds in 2d image space")
  Member "pq net a generative part seq2seq network for 3d shapes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "object driven text to image synthesis via adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "object centric image generation from layouts")
  Member "cvxnets learnable convex decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "implicit geometric regularization for learning shapes")
  Member "three d safari learning to estimate zebra pose shape and texture from images in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deformation aware unpaired image translation for pose estimation on laboratory animals")

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)