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: "forensic analysis of phone call networks"
  Abstract: In the context of preventing and fighting crime, the analysis of mobile phone traffic, among actors of a criminal network, is helpful in order to reconstruct illegal activities on the basis of the relationships connecting those specific individuals. Thus, forensic analysts and investigators require new advanced tools and techniques which allow them to manage these data in a meaningful and efficient way. In this paper we present LogAnalysis, a tool we developed to provide visual data representation and filtering, statistical analysis features and the possibility of a temporal analysis of mobile phone activities. Its adoption may help in unveiling the structure of a criminal network and the roles and dynamics of communications among its components. Using LogAnalysis, forensic investigators could deeply understand hierarchies within criminal organizations, for e.g., discovering central members who provide connections among different sub-groups, etc. Moreover, by analyzing the temporal evolution of the contacts among individuals, or by focusing on specific time windows they could acquire additional insights on the data they are analyzing. Finally, we put into evidence how the adoption of LogAnalysis may be crucial to solve real cases, providing as example a number of case studies inspired by real forensic investigations led by one of the authors.

Connected through hyperedge HE-1 (induced by source paper "network structure and resilience of mafia syndicates"):
  with members:
    - "detecting criminal organizations in mobile phone networks"
    - "enhancing community detection using a network weighting strategy"
    - "a novel measure of edge centrality in social networks"
    - "the anatomy of the facebook social graph"
    - "crawling facebook for social network analysis purposes"

Connected through hyperedge HE-2 (induced by source paper "ranking the importance level of intermediaries to a criminal using a reliance measure"):
  with members:
    - "using shortest path to discover criminal community"
    - "identifying a criminal s network of trust"
    - "identifying influential nodes in bipartite networks using the clustering coefficient"
    - "identifying high betweenness centrality nodes in large social networks"
    - "a novel measure of edge centrality in social networks"

Connected through hyperedge HE-3 (induced by source paper "visualizing criminal networks reconstructed from mobile phone records"):
  with members:
    - "mixing local and global information for community detection in large networks"
    - "detecting criminal organizations in mobile phone networks"
    - "enhancing community detection using a network weighting strategy"
    - "a novel measure of edge centrality in social networks"
    - "generalized louvain method for community detection in large networks"

Connected through hyperedge HE-4 (induced by source paper "scalable online betweenness centrality in evolving graphs"):
  with members:
    - "axioms for centrality"
    - "betweenness centrality incremental and faster"
    - "identifying high betweenness centrality nodes in large social networks"

Connected through hyperedge HE-5 (induced by source paper "betweenness centrality incremental and faster"):
  with members:
    - "scalable online betweenness centrality in evolving graphs"
    - "decremental all pairs all shortest paths and betweenness centrality"
    - "identifying high betweenness centrality nodes in large social networks"

Connected through hyperedge HE-6 (induced by source paper "identifying high betweenness centrality nodes in large social networks"):
  with members:
    - "small world file sharing communities"
    - "fast approximation of centrality"

Connected through hyperedge HE-7 (induced by source paper "a scalable heuristic for viral marketing under the tipping model"):
  with members:
    - "a model of internet topology using k shell decomposition"

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)