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: "principal patterns on graphs discovering coherent structures in datasets"
  Abstract: Graphs are now ubiquitous in almost every field of research. Recently, new research areas devoted to the analysis of graphs and data associated to their vertices have emerged. Focusing on dynamical processes, we propose a fast, robust and scalable framework for retrieving and analyzing recurring patterns of activity on graphs. Our method relies on a novel type of multilayer graph that encodes the spreading or propagation of events between successive time steps. We demonstrate the versatility of our method by applying it on three different real-world examples. Firstly, we study how rumor spreads on a social network. Secondly, we reveal congestion patterns of pedestrians in a train station. Finally, we show how patterns of audio playlists can be used in a recommender system. In each example, relevant information previously hidden in the data is extracted in a very efficient manner, emphasizing the scalability of our method. With a parallel implementation scaling linearly with the size of the dataset, our framework easily handles millions of nodes on a single commodity server.

Connected through hyperedge HE-1 (induced by source paper "anomaly detection in the dynamics of web and social networks"):
  with members:
    - "spatio temporal data mining a survey of problems and methods"

Connected through hyperedge HE-2 (induced by source paper "a time vertex signal processing framework"):
  with members:
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "fast robust pca on graphs"
    - "frequency analysis of temporal graph signals"
    - "towards stationary time vertex signal processing"
    - "pde approaches to graph analysis"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "discrete signal processing on graphs"

Connected through hyperedge HE-3 (induced by source paper "multilayer spectral graph clustering via convex layer aggregation"):
  with members:
    - "phase transitions and a model order selection criterion for spectral graph clustering"
    - "clustering network layers with the strata multilayer stochastic block model"
    - "information extraction from larger multi layer social networks"
    - "universal phase transition in community detectability under a stochastic block model"
    - "multi centrality graph spectral decompositions and their application to cyber intrusion detection"
    - "deep community detection"
    - "dynamic stochastic blockmodels for time evolving social networks"
    - "multi layer graph analysis for dynamic social networks"

Connected through hyperedge HE-4 (induced by source paper "big data challenges for the social sciences from society and opinion to replications"):
  with members:
    - "rising tides or rising stars dynamics of shared attention on twitter during media events"
    - "selection and influence in cultural dynamics"
    - "dynamical classes of collective attention in twitter"
    - "inferring networks of diffusion and influence"

Connected through hyperedge HE-5 (induced by source paper "identifying influential links for event propagation on twitter a network of networks approach"):
  with members:
    - "spectral projector based graph fourier transforms"
    - "diffusion and superposition distances for signals supported on networks"
    - "information diffusion and external influence in 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)