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: "centrality measures for networks with community structure"
  Abstract: Understanding the network structure, and finding out the influential nodes is a challenging issue in large networks. Identifying the most influential nodes in a network can be useful in many applications like immunization of nodes in case of epidemic spreading, during intentional attacks on complex networks. A lot of research is being done to devise centrality measures which could efficiently identify the most influential nodes in a network. There are two major approaches to this problem: On one hand, deterministic strategies that exploit knowledge about the overall network topology, while on the other end, random strategies are completely agnostic about the network structure. Centrality measures that can deal with a limited knowledge of the network structure are of prime importance. Indeed, in practice, information about the global structure of the overall network is rarely available or hard to acquire. Even if available, the structure of the network might be too large that it is too much computationally expensive to calculate global centrality measures. To that end, a centrality measure is proposed here that requires information only at the community level. Indeed, most of the real-world networks exhibit a community structure that can be exploited efficiently to discover the influential nodes. We performed a comparative evaluation of prominent global deterministic strategies together with stochastic strategies, an available and the proposed deterministic community-based strategy. Effectiveness of the proposed method is evaluated by performing experiments on synthetic and real-world networks with community structure in the case of immunization of nodes for epidemic control.

Connected through hyperedge HE-1 (induced by source paper "movienet a movie multilayer network model using visual and textual semantic cues"):
  with members:
    - "semantic flow in language networks"
    - "multilayer network model of movie script"
    - "centrality in modular networks"
    - "movie rating prediction using content based and link stream features"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "stream graphs and link streams for the modeling of interactions over time"
    - "face detection with the faster r cnn"
    - "dense captioning with joint inference and visual context"

Connected through hyperedge HE-2 (induced by source paper "introducing multilayer stream graphs and layer centralities"):
  with members:
    - "multilayer network model of movie script"
    - "stream graphs and link streams for the modeling of interactions over time"
    - "time centrality in dynamic complex networks"

Connected through hyperedge HE-3 (induced by source paper "centrality in modular networks"):
  with members:
    - "social centrality using network hierarchy and community structure"
    - "immunization of networks with non overlapping community structure"
    - "community based immunization strategies for epidemic control"
    - "towards realistic artificial benchmark for community detection algorithms evaluation"
    - "the role of strong and weak ties in facebook a community structure perspective"
    - "social structure of facebook networks"
    - "comparative evaluation of community detection algorithms a topological approach"
    - "a classification for community discovery methods in complex networks"

Connected through hyperedge HE-4 (induced by source paper "immunization of networks with non overlapping community structure"):
  with members:
    - "centrality in modular networks"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "community based immunization strategies for epidemic control"
    - "an empirical study of the relation between community structure and transitivity"
    - "towards realistic artificial benchmark for community detection algorithms evaluation"
    - "the role of strong and weak ties in facebook a community structure perspective"
    - "social structure of facebook networks"
    - "comparative evaluation of community detection algorithms a topological approach"

Connected through hyperedge HE-5 (induced by source paper "efficient edge rewiring strategies for enhancement in network capacity"):
  with members:
    - "effect of correlations on routing and modeling of time varying communication networks"
    - "time varying network modeling and its optimal routing strategy"
    - "effect of correlation on the traffic capacity of time varying communication network"
    - "combinatorial analysis of multiple networks"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-6 (induced by source paper "social centrality using network hierarchy and community structure"):
  with members:
    - "labelrank a stabilized label propagation algorithm for community detection in networks"
    - "axioms for centrality"
    - "truss decomposition in massive networks"
    - "k core decomposition of internet graphs hierarchies self similarity and measurement biases"

Connected through hyperedge HE-7 (induced by source paper "group based centrality for immunization of complex networks"):
  with members:
    - "identifying a set of influential spreaders in complex networks"
    - "how good is the shapley value based approach to the influence maximization problem"
    - "efficient computation of the shapley value for game theoretic network centrality"

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)