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: "a novel measure of edge centrality in social networks"
  Abstract: The problem of assigning centrality values to nodes and edges in graphs has been widely investigated during last years. Recently, a novel measure of node centrality has been proposed, called @k-path centrality index, which is based on the propagation of messages inside a network along paths consisting of at most @k edges. On the other hand, the importance of computing the centrality of edges has been put into evidence since 1970s by Anthonisse and, subsequently by Girvan and Newman. In this work we propose the generalization of the concept of @k-path centrality by defining the @k-path edge centrality, a measure of centrality introduced to compute the importance of edges. We provide an efficient algorithm, running in O(@km), being m the number of edges in the graph. Thus, our technique is feasible for large scale network analysis. Finally, the performance of our algorithm is analyzed, discussing the results obtained against large online social network datasets.

Connected through hyperedge HE-1 (induced by source paper "novel edge and density metrics for link cohesion"):
  with members:
    - "trusses and trapezes easily interpreted communities in social networks"
    - "motifs in temporal networks"
    - "socrates a system for scalable graph analytics"

Connected through hyperedge HE-2 (induced by source paper "sorec a social relation based centrality measure in mobile social networks"):
  with members:
    - "d2d big data content deliveries over wireless device to device sharing in large scale mobile networks"
    - "mixing local and global information for community detection in large networks"
    - "enhancing community detection using a network weighting strategy"

Connected through hyperedge HE-3 (induced by source paper "link transmission centrality in large scale social networks"):
  with members:
    - "detecting global bridges in networks"
    - "interplay between social influence and network centrality a comparative study on shapley centrality and single node influence centrality"
    - "understanding the influence of all nodes in a network"
    - "on facebook most ties are weak"

Connected through hyperedge HE-4 (induced by source paper "does william shakespeare really write hamlet knowledge representation learning with confidence"):
  with members:
    - "type constrained representation learning in knowledge graphs"
    - "modeling relation paths for representation learning of knowledge bases"

Connected through hyperedge HE-5 (induced by source paper "network structure and resilience of mafia syndicates"):
  with members:
    - "detecting criminal organizations in mobile phone networks"
    - "forensic analysis of phone call networks"
    - "enhancing community detection using a network weighting strategy"
    - "the anatomy of the facebook social graph"
    - "crawling facebook for social network analysis purposes"

Connected through hyperedge HE-6 (induced by source paper "privacy preserving schemes for ad hoc social networks a survey"):
  with members:
    - "technical privacy metrics a systematic survey"
    - "human behaviour as an aspect of cyber security assurance"
    - "privacy and data protection by design from policy to engineering"

Connected through hyperedge HE-7 (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"
    - "forensic analysis of phone call networks"
    - "identifying high betweenness centrality nodes in large social networks"

Connected through hyperedge HE-8 (induced by source paper "on facebook most ties are weak"):
  with members:
    - "the role of social networks in information diffusion"
    - "a large scale community structure analysis in facebook"
    - "crawling facebook for social network analysis purposes"
    - "four degrees of separation"
    - "weak ties subtle role of information diffusion in online social 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)