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: "darwini generating realistic large scale social graphs"
  Abstract: Synthetic graph generators facilitate research in graph algorithms and processing systems by providing access to data, for instance, graphs resembling social networks, while circumventing privacy and security concerns. Nevertheless, their practical value lies in their ability to capture important metrics of real graphs, such as degree distribution and clustering properties. Graph generators must also be able to produce such graphs at the scale of real-world industry graphs, that is, hundreds of billions or trillions of edges. #R##N#In this paper, we propose Darwini, a graph generator that captures a number of core characteristics of real graphs. Importantly, given a source graph, it can reproduce the degree distribution and, unlike existing approaches, the local clustering coefficient and joint-degree distributions. Furthermore, Darwini maintains metrics such node PageRank, eigenvalues and the K-core decomposition of a source graph. Comparing Darwini with state-of-the-art generative models, we show that it can reproduce these characteristics more accurately. Finally, we provide an open source implementation of our approach on the vertex-centric Apache Giraph model that allows us to create synthetic graphs with one trillion edges.

Connected through hyperedge HE-1 (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"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "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"
    - "an introduction to temporal graphs an algorithmic perspective"

Connected through hyperedge HE-2 (induced by source paper "survey of graph analysis applications"):
  with members:
    - "the ubiquity of large graphs and surprising challenges of graph processing"
    - "towards a property graph generator for benchmarking"
    - "a graph analysis of the linked data cloud"

Connected through hyperedge HE-3 (induced by source paper "towards a property graph generator for benchmarking"):
  with members:
    - "gmark schema driven generation of graphs and queries"
    - "a scalable generative graph model with community structure"

Connected through hyperedge HE-4 (induced by source paper "social hash partitioner a scalable distributed hypergraph partitioner"):
  with members:
    - "recent advances in graph partitioning"

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)