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: "evolution of directed triangle motifs in the google osn"
  Abstract: Motifs are a fundamental building block and distinguishing feature of networks. While characteristic motif distribution have been found in many networks, very little is known today about the evolution of network motifs. This paper studies the most important motifs in social networks, triangles, and how directed triangle motifs change over time. Our chosen subject is one of the largest Online Social Networks, Google+. Google+ has two distinguishing features that make it particularly interesting: (1) it is a directed network, which yields a rich set of triangle motifs, and (2) it is a young and fast evolving network, whose role in the OSN space is still not fully understood. For the purpose of this study, we crawled the network over a time period of six weeks, collecting several snapshots. We find that some triangle types display significant dynamics, e.g., for some specific initial types, up to 20% of the instances evolve to other types. Due to the fast growth of the OSN in the observed time period, many new triangles emerge. We also observe that many triangles evolve into less-connected motifs (with less edges), suggesting that growth also comes with pruning. We complement the topological study by also considering publicly available user profile data (mostly geographic locations). The corresponding results shed some light on the semantics of the triangle motifs. Indeed, we find that users in more symmetric triangle motifs live closer together, indicating more personal relationships. In contrast, asymmetric links in motifs often point to faraway users with a high in-degree (celebrities).

Connected through hyperedge HE-1 (induced by source paper "subgraph networks with application to structural feature space expansion"):
  with members:
    - "adaptive graph convolutional neural networks"
    - "motifs in temporal networks"
    - "detecting strong ties using network motifs"
    - "distributed representation of subgraphs"
    - "graph2vec learning distributed representations of graphs"
    - "wide deep learning for recommender systems"
    - "higher order organization of complex networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-2 (induced by source paper "fleet butterfly estimation from a bipartite graph stream"):
  with members:
    - "parallel algorithms for butterfly computations"
    - "vertex priority based butterfly counting for large scale bipartite networks"
    - "butterfly counting in bipartite networks"
    - "edge sample and discard a new algorithm for counting triangles in large dynamic graphs"
    - "on sampling from massive graph streams"
    - "peeling bipartite networks for dense subgraph discovery"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "measuring and modeling bipartite graphs with community structure"

Connected through hyperedge HE-3 (induced by source paper "counting butterfies from a large bipartite graph stream"):
  with members:
    - "parallel algorithms for butterfly computations"
    - "on sampling from massive graph streams"
    - "peeling bipartite networks for dense subgraph discovery"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "measuring and modeling bipartite graphs with community structure"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "triangle counting in dynamic graph streams"
    - "parallel triangle counting in massive streaming graphs"

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)