human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "motifs in temporal networks"):
  Abstract: Networks are a fundamental tool for modeling complex systems in a variety of domains including social and communication networks as well as biology and neuroscience. The counts of small subgraph patterns in networks, called network motifs, are crucial to understanding the structure and function of these systems. However, the role of network motifs for temporal networks, which contain many timestamped links between nodes, is not well understood.   Here we develop a notion of a temporal network motif as an elementary unit of temporal networks and provide a general methodology for counting such motifs. We define temporal network motifs as induced subgraphs on sequences of edges, design several fast algorithms for counting temporal network motifs, and prove their runtime complexity. We also show that our fast algorithms achieve 1.3x to 56.5x speedups compared to a baseline method.   We use our algorithms to count temporal network motifs in a variety of real-world datasets. Results show that networks from different domains have significantly different motif frequencies, whereas networks from the same domain tend to have similar motif frequencies. We also find that measuring motif counts at various time scales reveals different behavior.

Includes members:
  Member "escape efficiently counting all 5 vertex subgraphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "higher order organization of complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "assembling thefacebook using heterogeneity to understand online social network assembly":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning resolution parameters for graph clustering")
  Member "tensor spectral clustering for partitioning higher order network structures":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pasta a parallel sparse tensor algorithm benchmark suite")
  Member "subgraph frequencies mapping the empirical and extremal geography of large graph collections":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "parallel algorithms for small subgraph counting")

Question: Please tell me which class the source paper 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)