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: "growing graphs from hyperedge replacement graph grammars"
  Abstract: Discovering the underlying structures present in large real world graphs is a fundamental scientific problem. In this paper we show that a graph's clique tree can be used to extract a hyperedge replacement grammar. If we store an ordering from the extraction process, the extracted graph grammar is guaranteed to generate an isomorphic copy of the original graph. Or, a stochastic application of the graph grammar rules can be used to quickly create random graphs. In experiments on large real world networks, we show that random graphs, generated from extracted graph grammars, exhibit a wide range of properties that are very similar to the original graphs. In addition to graph properties like degree or eigenvector centrality, what a graph ``looks like'' ultimately depends on small details in local graph substructures that are difficult to define at a global level. We show that our generative graph model is able to preserve these local substructures when generating new graphs and performs well on new and difficult tests of model robustness.

Connected through hyperedge HE-1 (induced by source paper "selfies a robust representation of semantically constrained graphs with an example application in chemistry"):
  with members:
    - "deep learning for molecular design a review of the state of the art"
    - "interpretable deep learning in drug discovery"
    - "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"
    - "deep reinforcement learning for de novo drug design"
    - "isolating sources of disentanglement in variational autoencoders"
    - "learning deep generative models of graphs"

Connected through hyperedge HE-2 (induced by source paper "modeling graphs with vertex replacement grammars"):
  with members:
    - "from louvain to leiden guaranteeing well connected communities"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "graph embedding techniques applications and performance a survey"
    - "learning hyperedge replacement grammars for graph generation"
    - "node2vec scalable feature learning for networks"
    - "an empirical comparison of the summarization power of graph clustering methods"
    - "line large scale information network embedding"

Connected through hyperedge HE-3 (induced by source paper "towards interpretable graph modeling with vertex replacement grammars"):
  with members:
    - "edge replacement grammars a formal language approach for generating graphs"
    - "modeling graphs with vertex replacement grammars"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "graph embedding techniques applications and performance a survey"
    - "learning hyperedge replacement grammars for graph generation"
    - "motifs in temporal networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-4 (induced by source paper "edge replacement grammars a formal language approach for generating graphs"):
  with members:
    - "a scalable generative graph model with community structure"

Connected through hyperedge HE-5 (induced by source paper "learning deep generative models of graphs"):
  with members:
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "molecular de novo design through deep reinforcement learning"
    - "neural message passing for quantum chemistry"
    - "molecular generation with recurrent neural networks rnns"
    - "smiles enumeration as data augmentation for neural network modeling of molecules"
    - "generating focussed molecule libraries for drug discovery with recurrent neural networks"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-6 (induced by source paper "growing better graphs with latent variable probabilistic graph grammars"):
  with members:
    - "the infinity mirror test for analyzing the robustness of graph generators"
    - "tree decompositions and social graphs"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"
    - "a complete anytime algorithm for treewidth"
    - "community structure and scale free collections of erdős renyi graphs"

Connected through hyperedge HE-7 (induced by source paper "molecular hypergraph grammar with its application to molecular optimization"):
  with members:
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "optimization of molecules via deep reinforcement learning"
    - "junction tree variational autoencoder for molecular graph generation"
    - "syntax directed variational autoencoder for structured data"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "learning hyperedge replacement grammars for graph generation"):
  with members:
    - "the infinity mirror test for analyzing the robustness of graph generators"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"

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.SI(Social and Information Networks)