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: "an optimal and progressive approach to online search of top k influential communities"
  Abstract: Community search over large graphs is a fundamental problem in graph analysis. Recent studies propose to compute top-k influential communities, where each reported community not only is a cohesive subgraph but also has a high influence value. The existing approaches to the problem of top-k influential community search can be categorized as index-based algorithms and online search algorithms without indexes. The index-based algorithms, although being very efficient in conducting community searches, need to pre-compute a special-purpose index and only work for one built-in vertex weight vector. In this paper, we investigate on-line search approaches and propose an instance-optimal algorithm LocalSearch whose time complexity is linearly proportional to the size of the smallest subgraph that a correct algorithm needs to access without indexes. In addition, we also propose techniques to make LocalSearch progressively compute and report the communities in decreasing influence value order such that k does not need to be specified. Moreover, we extend our framework to the general case of top-k influential community search regarding other cohesiveness measures. Extensive empirical studies on real graphs demonstrate that our algorithms outperform the existing online search algorithms by several orders of magnitude.

Connected through hyperedge HE-1 (induced by source paper "a survey of community search over big graphs"):
  with members:
    - "exploring communities in large profiled graphs"
    - "efficient algorithms for densest subgraph discovery"
    - "community discovery in dynamic networks a survey"
    - "pp dblp modeling and generating attributed public private networks with dblp"
    - "local community detection in dynamic networks"
    - "diversified coherent core search on multi layer graphs"
    - "network community detection a review and visual survey"
    - "local community extraction in directed networks"

Connected through hyperedge HE-2 (induced by source paper "keyword aware influential community search in large attributed graphs"):
  with members:
    - "exploring communities in large profiled graphs"
    - "a survey of community search over big graphs"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "truss decomposition in massive networks"
    - "efficient community detection in large networks using content and links"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-3 (induced by source paper "fast algorithms for intimate core group search in weighted graphs"):
  with members:
    - "k core minimization a game theoretic approach"
    - "a survey of community search over big graphs"
    - "the minimum wiener connector problem"
    - "approximate closest community search in networks"
    - "truss decomposition in massive networks"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-4 (induced by source paper "finding effective geo social group for impromptu activity with multiple demands"):
  with members:
    - "when engagement meets similarity efficient k r core computation on social networks"
    - "socio spatial group queries for impromptu activity planning"

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.DB(Databases)