human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a survey of community search over big graphs"):
  Abstract: With the rapid development of information technologies, various big graphs are prevalent in many real applications (e.g., social media and knowledge bases). An important component of these graphs is the network community. Essentially, a community is a group of vertices which are densely connected internally. Community retrieval can be used in many real applications, such as event organization, friend recommendation, and so on. Consequently, how to efficiently find high-quality communities from big graphs is an important research topic in the era of big data. Recently a large group of research works, called community search, have been proposed. They aim to provide efficient solutions for searching high-quality communities from large networks in real-time. Nevertheless, these works focus on different types of graphs and formulate communities in different manners, and thus it is desirable to have a comprehensive review of these works. #R##N#In this survey, we conduct a thorough review of existing community search works. Moreover, we analyze and compare the quality of communities under their models, and the performance of different solutions. Furthermore, we point out new research directions. This survey does not only help researchers to have a better understanding of existing community search solutions, but also provides practitioners a better judgment on choosing the proper solutions.

Includes members:
  Member "exploring communities in large profiled graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient algorithms for densest subgraph discovery")
  Member "efficient algorithms for densest subgraph discovery":
    also connected through other hyperedges:
      (none)
  Member "community discovery in dynamic networks a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "role detection in bicycle sharing networks using multilayer stochastic block models")
  Member "pp dblp modeling and generating attributed public private networks with dblp":
    also connected through other hyperedges:
      (none)
  Member "an optimal and progressive approach to online search of top k influential communities":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "keyword aware influential community search in large attributed graphs")
  Member "local community detection in dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "finding events in temporal networks segmentation meets densest subgraph discovery")
  Member "diversified coherent core search on multi layer graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "core decomposition in multilayer networks theory algorithms and applications")
  Member "network community detection a review and visual survey":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the source paper 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)