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: "rethinking centrality the role of dynamical processes in social network analysis"
  Abstract: Many popular measures used in social network analysis, including centrality, are based on the random walk. The random walk is a model of a stochastic process where a node interacts with one other node at a time. However, the random walk may not be appropriate for modeling social phenomena, including epidemics and information diffusion, in which one node may interact with many others at the same time, for example, by broadcasting the virus or information to its neighbors. To produce meaningful results, social network analysis algorithms have to take into account the nature of interactions between the nodes. In this paper we classify dynamical processes as conservative and non-conservative and relate them to well-known measures of centrality used in network analysis: PageRank and Alpha-Centrality. We demonstrate, by ranking users in online social networks used for broadcasting information, that non-conservative Alpha-Centrality generally leads to a better agreement with an empirical ranking scheme than the conservative PageRank.

Connected through hyperedge HE-1 (induced by source paper "centralities for networks with consumable resources"):
  with members:
    - "relaxation based coarsening for multilevel hypergraph partitioning"
    - "utility maximization framework for opportunistic wireless electric vehicle charging"
    - "generating realistic scaled complex networks"
    - "lurking in social networks topology based analysis and ranking methods"
    - "multiscale network generation"

Connected through hyperedge HE-2 (induced by source paper "understanding and forecasting lifecycle events in information cascades"):
  with members:
    - "toward order of magnitude cascade prediction"
    - "understanding the influence of all nodes in a network"
    - "from micro to macro uncovering and predicting information cascading process with behavioral dynamics"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "the bursty dynamics of the twitter information network"
    - "can cascades be predicted"
    - "limited attention and centrality in social networks"

Connected through hyperedge HE-3 (induced by source paper "graph filters and the z laplacian"):
  with members:
    - "discrete signal processing on graphs frequency 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-4 (induced by source paper "interplay between social influence and network centrality a comparative study on shapley centrality and single node influence centrality"):
  with members:
    - "network essence pagerank completion and centrality conforming markov chains"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "axioms for centrality"
    - "efficient computation of the shapley value for game theoretic network centrality"
    - "maximizing social influence in nearly optimal time"

Connected through hyperedge HE-5 (induced by source paper "evolving twitter an experimental analysis of graph properties of the social graph"):
  with members:
    - "aggregate characterization of user behavior in twitter and analysis of the retweet graph"
    - "two types of well followed users in the followership networks of twitter"
    - "four degrees of separation"

Connected through hyperedge HE-6 (induced by source paper "limited attention and centrality in social networks"):
  with members:
    - "google matrix of twitter"
    - "parameterized centrality metric for network analysis"
    - "non conservative diffusion and its application to social network analysis"
    - "predicting influential users in online social networks"
    - "crowdsourcing attention and productivity"
    - "novelty and collective attention"

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)