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: "truss decomposition in massive networks"
  Abstract: The k-truss is a type of cohesive subgraphs proposed recently for the study of networks. While the problem of computing most cohesive subgraphs is NP-hard, there exists a polynomial time algorithm for computing k-truss. Compared with k-core which is also efficient to compute, k-truss represents the "core" of a k-core that keeps the key information of, while filtering out less important information from, the k-core. However, existing algorithms for computing k-truss are inefficient for handling today's massive networks. We first improve the existing in-memory algorithm for computing k-truss in networks of moderate size. Then, we propose two I/O-efficient algorithms to handle massive networks that cannot fit in main memory. Our experiments on real datasets verify the efficiency of our algorithms and the value of k-truss.

Connected through hyperedge HE-1 (induced by source paper "efficient bitruss decomposition for large scale bipartite graphs"):
  with members:
    - "parallel algorithms for butterfly computations"
    - "vertex priority based butterfly counting for large scale bipartite networks"
    - "butterfly counting in bipartite networks"
    - "peeling bipartite networks for dense subgraph discovery"
    - "generalized two mode cores"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "distance generalized core decomposition"):
  with members:
    - "mining maximal span cores from temporal networks"
    - "when engagement meets similarity efficient k r core computation on social networks"
    - "density friendly graph decomposition"
    - "the minimum wiener connector problem"
    - "finding the hierarchy of dense subgraphs using nucleus decompositions"
    - "social resilience in online communities the autopsy of friendster"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"
    - "four degrees of separation"

Connected through hyperedge HE-4 (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"
    - "an optimal and progressive approach to online search of top k influential communities"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "efficient community detection in large networks using content and links"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-5 (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"
    - "an optimal and progressive approach to online search of top k influential communities"
    - "the minimum wiener connector problem"
    - "approximate closest community search in networks"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-6 (induced by source paper "finding route hotspots in large labeled networks"):
  with members:
    - "finding theme communities from database networks"
    - "when engagement meets similarity efficient k r core computation on social networks"
    - "efficient discovery of association rules and frequent itemsets through sampling with tight performance guarantees"
    - "discovery of convoys in trajectory databases"

Connected through hyperedge HE-7 (induced by source paper "streaming and batch algorithms for truss decomposition"):
  with members:
    - "snap a general purpose network analysis and graph mining library"
    - "approximate closest community search in networks"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-8 (induced by source paper "critical edge identification a k truss based model"):
  with members:
    - "polynomial kernels and wideness properties of nowhere dense graph classes"
    - "i o efficient core graph decomposition at web scale"

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)