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: "lurking in social networks topology based analysis and ranking methods"
  Abstract: The massive presence of silent members in online communities, the so-called lurkers, has long attracted the attention of researchers in social science, cognitive psychology, and computer–human interaction. However, the study of lurking phenomena represents an unexplored opportunity of research in data mining, information retrieval and related fields. In this paper, we take a first step towards the formal specification and analysis of lurking in social networks. We address the new problem of lurker ranking and propose the first centrality methods specifically conceived for ranking lurkers in social networks. Our approach utilizes only the network topology without probing into text contents or user relationships related to media. Using Twitter, Flickr, FriendFeed and GooglePlus as cases in point, our methods’ performance was evaluated against data-driven rankings as well as existing centrality methods, including the classic PageRank and alpha-centrality. Empirical evidence has shown the significance of our lurker ranking approach, and its uniqueness in effectively identifying and ranking lurkers in an online social network.

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"
    - "rethinking centrality the role of dynamical processes in social network analysis"
    - "multiscale network generation"

Connected through hyperedge HE-2 (induced by source paper "adult content in social live streaming services characterizing deviant users and relationships"):
  with members:
    - "on the behaviour of deviant communities in online social networks"
    - "deep residual learning for image recognition"
    - "intriguing properties of neural networks"
    - "towards unbiased bfs sampling"

Connected through hyperedge HE-3 (induced by source paper "topology driven diversity for targeted influence maximization with application to user engagement in social networks"):
  with members:
    - "time aware analysis and ranking of lurkers in social networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "maximizing social influence in nearly optimal time"

Connected through hyperedge HE-4 (induced by source paper "modeling evolutionary dynamics of lurking in social networks"):
  with members:
    - "engaging with massive online courses"

Connected through hyperedge HE-5 (induced by source paper "time aware analysis and ranking of lurkers in social networks"):
  with members:
    - "online popularity and topical interests through the lens of instagram"
    - "social resilience in online communities the autopsy of friendster"
    - "analyzing user behavior across social sharing environments"
    - "dynamical classes of collective attention in twitter"

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)