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: "interplay between social influence and network centrality a comparative study on shapley centrality and single node influence centrality"
  Abstract: We study network centrality based on dynamic influence propagation models in social networks. To illustrate our integrated mathematical-algorithmic approach for understanding the fundamental interplay between dynamic influence processes and static network structures, we focus on two basic centrality measures: (a) Single Node Influence (SNI) centrality, which measures each node's significance by its influence spread; and (b) Shapley Centrality, which uses the Shapley value of the influence spread function --- formulated based on a fundamental cooperative-game-theoretical concept --- to measure the significance of nodes. We present a comprehensive comparative study of these two centrality measures. Mathematically, we present axiomatic characterizations, which precisely capture the essence of these two centrality measures and their fundamental differences. Algorithmically, we provide scalable algorithms for approximating them for a large family of social-influence instances. Empirically, we demonstrate their similarity and differences in a number of real-world social networks, as well as the efficiency of our scalable algorithms. Our results shed light on their applicability: SNI centrality is suitable for assessing individual influence in isolation while Shapley centrality assesses individuals' performance in group influence settings.

Connected through hyperedge HE-1 (induced by source paper "maximizing influence based group shapley centrality"):
  with members:
    - "game theoretic network centrality a review"
    - "computing top k closeness centrality faster in unweighted graphs"
    - "scaling up group closeness maximization"
    - "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"
    - "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-2 (induced by source paper "influence maximization with spontaneous user adoption"):
  with members:
    - "an issue in the martingale analysis of the influence maximization algorithm imm"
    - "multi round influence maximization"
    - "robust influence maximization"
    - "robust influence maximization"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "maximizing social influence in nearly optimal time"

Connected through hyperedge HE-3 (induced by source paper "multiscale evolutionary perturbation attack on community detection"):
  with members:
    - "adversarial attacks on node embeddings via graph poisoning"
    - "ga based q attack on community detection"
    - "hiding individuals and communities in a social network"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "clustering and community detection in directed networks a survey"
    - "multistep greedy algorithm identifies community structure in real world and computer generated networks"

Connected through hyperedge HE-4 (induced by source paper "an issue in the martingale analysis of the influence maximization algorithm imm"):
  with members:
    - "multi round influence maximization"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "maximizing social influence in nearly optimal time"
    - "irie scalable and robust influence maximization in social networks"

Connected through hyperedge HE-5 (induced by source paper "link transmission centrality in large scale social networks"):
  with members:
    - "detecting global bridges in networks"
    - "understanding the influence of all nodes in a network"
    - "on facebook most ties are weak"
    - "a novel measure of edge centrality in social networks"

Connected through hyperedge HE-6 (induced by source paper "a systematic framework and characterization of influence based network centrality"):
  with members:
    - "axioms for centrality"

Connected through hyperedge HE-7 (induced by source paper "network essence pagerank completion and centrality conforming markov chains"):
  with members:
    - "the ground truth about metadata and community detection in networks"
    - "network composition from multi layer data"
    - "approximate undirected maximum flows in o m polylog n time"
    - "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations"
    - "graphical models for game theory"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "efficient computation of the shapley value for game theoretic network centrality"
    - "nearly maximum flows in nearly linear time"

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)