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: "analysing collective behaviour in temporal networks using event graphs and temporal motifs"
  Abstract: Historically studies of behaviour on networks have focused on the behaviour of individuals (node-based) or on the aggregate behaviour of the entire network. We propose a new method to decompose a temporal network into macroscale components and to analyse the behaviour of these components, or collectives of nodes, across time. This method utilises all available information in the temporal network (i.e. no temporal aggregation), combining both topological and temporal structure using temporal motifs and inter-event times. This allows us create an embedding of a temporal network in order to describe behaviour over time and at different timescales. We illustrate this method using an example of digital communication data collected from an online social network.

Connected through hyperedge HE-1 (induced by source paper "gl2vec learning feature representation using graphlets for directed networks"):
  with members:
    - "network classification in temporal networks using motifs"
    - "classifying conversation in digital communication"
    - "struc2vec learning node representations from structural identity"
    - "motifs in temporal networks"
    - "representation learning on graphs methods and applications"
    - "graph2vec learning distributed representations of graphs"
    - "xgboost a scalable tree boosting system"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-2 (induced by source paper "event graphs advances and applications of second order time unfolded temporal network models"):
  with members:
    - "the temporal event graph"
    - "simplicial closure and higher order link prediction"
    - "motifs in temporal networks"
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"
    - "representing higher order dependencies in networks"
    - "an introduction to temporal graphs an algorithmic perspective"
    - "spectral redemption in clustering sparse networks"

Connected through hyperedge HE-3 (induced by source paper "network classification in temporal networks using motifs"):
  with members:
    - "struc2vec learning node representations from structural identity"
    - "motifs in temporal networks"
    - "xgboost a scalable tree boosting system"
    - "node2vec scalable feature learning for networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "deepwalk online learning of social representations"
    - "loopy belief propagation for approximate inference an empirical study"

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)