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 fast order based approach for core maintenance"
  Abstract: Graphs have been widely used in many applications such as social networks, collaboration networks, and biological networks. One important graph analytics is to explore cohesive subgraphs in a large graph. Among several cohesive subgraphs studied, k-core is one that can be computed in linear time for a static graph. Since graphs are evolving in real applications, in this paper, we study core maintenance which is to reduce the computational cost to compute k-cores for a graph when graphs are updated from time to time dynamically. We identify drawbacks of the existing efficient algorithm, which needs a large search space to find the vertices that need to be updated, and has high overhead to maintain the index built, when a graph is updated. We propose a new order-based approach to maintain an order, called k-order, among vertices, while a graph is updated. Our new algorithm can significantly outperform the state-of-the-art algorithm up to 3 orders of magnitude for the 11 large real graphs tested. We report our findings in this paper.

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"
    - "an optimal and progressive approach to online search of top k influential communities"
    - "local community detection in dynamic networks"
    - "diversified coherent core search on multi layer graphs"
    - "network community detection a review and visual survey"

Connected through hyperedge HE-2 (induced by source paper "k core maximization through edge additions"):
  with members:
    - "k core minimization a game theoretic approach"
    - "preventing unraveling in social networks gets harder"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-3 (induced by source paper "parameter free structural diversity search"):
  with members:
    - "discovering social circles in ego networks"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-4 (induced by source paper "core decomposition in multilayer networks theory algorithms and applications"):
  with members:
    - "on finding dense common subgraphs"
    - "mining maximal span cores from temporal networks"
    - "to be connected or not to be connected that is the minimum inefficiency subgraph problem"
    - "diversified coherent core search on multi layer graphs"
    - "best friends forever bff finding lasting dense subgraphs"
    - "when engagement meets similarity efficient k r core computation on social networks"
    - "i o efficient core graph decomposition at web scale"
    - "the minimum wiener connector problem"

Connected through hyperedge HE-5 (induced by source paper "top k densest subgraphs in sliding window graph streams"):
  with members:
    - "densest subgraph in dynamic graph streams"
    - "density friendly graph decomposition"
    - "when two choices are not enough balancing at scale in distributed stream processing"
    - "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"
    - "densest subgraph in streaming and mapreduce"
    - "defining and evaluating network communities based on ground truth"

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)