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: "gram scalable generative models for graphs with graph attention mechanism"
  Abstract: Graphs are ubiquitous real-world data structures, and generative models that can approximate distributions over graphs and derive samples from it have significant importance. There are several known challenges in graph generation tasks, and scalability handling large graphs and datasets is one of the most important for applications in a wide range of real-world domains. Although an increasing number of graph generative models have been proposed in the field of machine learning that have demonstrated impressive results in several tasks, scalability is still an unresolved problem owing to the complex generation process or difficulty in training parallelization. In this work, we first define scalability from three different perspectives: number of nodes, data, and node/edge labels, and then we propose GRAM, a generative model for real-world graphs that is scalable in all the three contexts, especially on training. We aim to achieve scalability by employing a novel graph attention mechanism, formulating the likelihood of graphs in a simple and general manner and utilizing the properties of real-world graphs such as community structure and sparseness of edges. Furthermore, we construct a non-domain-specific evaluation metric in node/edge-labeled graph generation tasks that combine a graph kernel and Maximum Mean Discrepancy. Our experiments on real-world graph datasets showed that our models can scale up to large graphs and datasets that baseline models had difficulty handling, and demonstrated results that were competitive with or superior than the baseline methods.

Connected through hyperedge HE-1 (induced by source paper "graphgen a scalable approach to domain agnostic labeled graph generation"):
  with members:
    - "deepnc deep generative network completion"
    - "d vae a variational autoencoder for directed acyclic graphs"
    - "labeled graph generative adversarial networks"
    - "molecularrnn generating realistic molecular graphs with optimized properties"
    - "efficient graph generation with graph recurrent attention networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "constrained graph variational autoencoders for molecule design"
    - "graphrnn generating realistic graphs with deep auto regressive models"

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)