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 "polygen an autoregressive generative model of 3d meshes"):
  Abstract: Polygon meshes are an efficient representation of 3D geometry, and are of central importance in computer graphics, robotics and games development. Existing learning-based approaches have avoided the challenges of working with 3D meshes, instead using alternative object representations that are more compatible with neural architectures and training approaches. We present an approach which models the mesh directly, predicting mesh vertices and faces sequentially using a Transformer-based architecture. Our model can condition on a range of inputs, including object classes, voxels, and images, and because the model is probabilistic it can produce samples that capture uncertainty in ambiguous scenarios. We show that the model is capable of producing high-quality, usable meshes, and establish log-likelihood benchmarks for the mesh-modelling task. We also evaluate the conditional models on surface reconstruction metrics against alternative methods, and demonstrate competitive performance despite not training directly on this task.

Includes members:
  Member "pointflow 3d point cloud generation with continuous normalizing flows":
    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 "the curious case of neural text degeneration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an empirical investigation of pre trained transformer language models for open domain dialogue generation")
  Member "stabilizing transformers for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transformer based reinforcement learning for games")
  Member "generating long sequences with sparse transformers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meta embeddings based on self attention")
  Member "image transformer":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial code learning for image generation")
  Member "graphrnn generating realistic graphs with deep auto regressive models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchical generation of molecular graphs using structural motifs")
  Member "generating high fidelity images with subscale pixel networks and multidimensional upscaling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "blur noise and compression robust generative adversarial networks")

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.GR(Graphics)