human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "graphrnn generating realistic graphs with deep auto regressive models"
  Abstract: Modeling and generating graphs is fundamental for studying networks in biology, engineering, and social sciences. However, modeling complex distributions over graphs and then efficiently sampling from these distributions is challenging due to the non-unique, high-dimensional nature of graphs and the complex, non-local dependencies that exist between edges in a given graph. Here we propose GraphRNN, a deep autoregressive model that addresses the above challenges and approximates any distribution of graphs with minimal assumptions about their structure. GraphRNN learns to generate graphs by training on a representative set of graphs and decomposes the graph generation process into a sequence of node and edge formations, conditioned on the graph structure generated so far. #R##N#In order to quantitatively evaluate the performance of GraphRNN, we introduce a benchmark suite of datasets, baselines and novel evaluation metrics based on Maximum Mean Discrepancy, which measure distances between sets of graphs. Our experiments show that GraphRNN significantly outperforms all baselines, learning to generate diverse graphs that match the structural characteristics of a target set, while also scaling to graphs 50 times larger than previous deep models.

Connected through hyperedge HE-1 (induced by source paper "hierarchical generation of molecular graphs using structural motifs"):
  with members:
    - "discrete object generation with reversible inductive construction"
    - "graph u nets"
    - "conditional molecular design with deep generative models"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "constrained graph variational autoencoders for molecule design"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "hierarchical graph representation learning with differentiable pooling"

Connected through hyperedge HE-2 (induced by source paper "polygen an autoregressive generative model of 3d meshes"):
  with members:
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "the curious case of neural text degeneration"
    - "stabilizing transformers for reinforcement learning"
    - "generating long sequences with sparse transformers"
    - "image transformer"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "pointgrow autoregressively learned point cloud generation with self attention"

Connected through hyperedge HE-3 (induced by source paper "composing molecules with multiple property constraints"):
  with members:
    - "functional transparency for structured data a game theoretic approach"
    - "discrete object generation with reversible inductive construction"
    - "conditional molecular design with deep generative models"
    - "gnnexplainer generating explanations for graph neural networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "constrained graph variational autoencoders for molecule design"
    - "molecular hypergraph grammar with its application to molecular optimization"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"

Connected through hyperedge HE-4 (induced by source paper "an end to end graph convolutional kernel support vector machine"):
  with members:
    - "scalable global alignment graph kernel using random features from node embedding to graph embedding"
    - "a fair comparison of graph neural networks for graph classification"
    - "on graph classification networks datasets and baselines"
    - "fast graph representation learning with pytorch geometric"
    - "learning discrete structures for graph neural networks"
    - "a comprehensive survey on graph neural networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "deep learning on graphs a survey"

Connected through hyperedge HE-5 (induced by source paper "a deep generative model for graph layout"):
  with members:
    - "evaluating the readability of force directed graph layouts a deep learning approach"
    - "on the convergence of adam and beyond"
    - "aesthetic discrimination of graph layouts"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "hierarchical graph representation learning with differentiable pooling"
    - "representation learning on graphs with jumping knowledge networks"
    - "automatic chemical design using a data driven continuous representation of molecules"

Connected through hyperedge HE-6 (induced by source paper "graphaf a flow based autoregressive model for molecular graph generation"):
  with members:
    - "graph normalizing flows"
    - "molecularrnn generating realistic molecular graphs with optimized properties"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "molecular sets moses a benchmarking platform for molecular generation models"
    - "junction tree variational autoencoder for molecular graph generation"

Connected through hyperedge HE-7 (induced by source paper "deterministic decoding for discrete data in variational autoencoders"):
  with members:
    - "from variational to deterministic autoencoders"
    - "a prior of a googol gaussians a tensor ring induced prior for generative models"
    - "hierarchical generative modeling for controllable speech synthesis"
    - "junction tree variational autoencoder for molecular graph generation"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "vae with a vampprior"
    - "seqgan sequence generative adversarial nets with policy gradient"

Connected through hyperedge HE-8 (induced by source paper "graph generators state of the art and open challenges"):
  with members:
    - "a survey on theoretical advances of community detection in networks"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "learning deep generative models of graphs"
    - "gmark schema driven generation of graphs and queries"
    - "motifs in temporal networks"
    - "g core a core for future graph query languages"
    - "darwini generating realistic large scale social graphs"
    - "an introduction to temporal graphs an algorithmic perspective"

Question: Please tell me which class the center node 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.LG(Machine Learning)