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: "subgraph isomorphism in temporal networks"
  Abstract: Temporal information is increasingly available as part of large network data sets. This information reveals sequences of link activations between network entities, which can expose underlying processes in the data. Examples include the dissemination of information through a social network, the propagation of musical ideas in a music sampling network, and the spread of a disease via contacts between infected and susceptible individuals. The search for these more meaningful patterns may be formulated as a time-respecting subgraph isomorphism problem. Our set of query graphs include an enumeration of small random graphs and fan-out-fan-in structures, all composed of time-respecting paths. We explore three methods of solving the problem, which differ in how they exploit temporal and topological information. One approach extracts all subgraphs that have the temporal properties we require and then performs subgraph isomorphism testing on each subgraph. Another approach performs subgraph isomorphism testing first with temporal post-filtering, while the other is a hybrid approach that uses temporal information during the search. We empirically demonstrate the hybrid approach to be more efficient than the others, over a range of network data sets. These data come from communication and social networks, up to interactions in size.

Connected through hyperedge HE-1 (induced by source paper "flow computation in temporal interaction networks"):
  with members:
    - "flow motifs in interaction networks"
    - "motifs in temporal networks"
    - "efficient subgraph matching on billion node graphs"

Connected through hyperedge HE-2 (induced by source paper "a chronological edge driven approach to temporal subgraph isomorphism"):
  with members:
    - "motifs in temporal networks"

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)