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: "i o efficient core graph decomposition at web scale"
  Abstract: Core decomposition is a fundamental graph problem with a large number of applications. Most existing approaches for core decomposition assume that the graph is kept in memory of a machine. Nevertheless, many real-world graphs are big and may not reside in memory. In the literature, there is only one work for I/O efficient core decomposition that avoids loading the whole graph in memory. However, this approach is not scalable to handle big graphs because it cannot bound the memory size and may load most parts of the graph in memory. In addition, this approach can hardly handle graph updates. In this paper, we study I/O efficient core decomposition following a semi-external model, which only allows node information to be loaded in memory. This model works well in many web-scale graphs. We propose a semi-external algorithm and two optimized algorithms for I/O efficient core decomposition using very simple structures and data access model. To handle dynamic graph updates, we show that our algorithm can be naturally extended to handle edge deletion. We also propose an I/O efficient core maintenance algorithm to handle edge insertion, and an improved algorithm to further reduce I/O and CPU cost by investigating some new graph properties. We conduct extensive experiments on 12 real large graphs. Our optimal algorithm significantly outperform the existing I/O efficient algorithm in terms of both processing time and memory consumption. In many memory-resident graphs, our algorithms for both core decomposition and maintenance can even outperform the in-memory algorithm due to the simple structures and data access model used. Our algorithms are very scalable to handle web-scale graphs. As an example, we are the first to handle a web graph with 978.5 million nodes and 42.6 billion edges using less than 4.2 GB memory.

Connected through hyperedge HE-1 (induced by source paper "critical edge identification a k truss based model"):
  with members:
    - "polynomial kernels and wideness properties of nowhere dense graph classes"
    - "truss decomposition in massive networks"

Connected through hyperedge HE-2 (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"
    - "a fast order based approach for core maintenance"
    - "the minimum wiener connector problem"

Connected through hyperedge HE-3 (induced by source paper "when engagement meets similarity efficient k r core computation on social networks"):
  with members:
    - "towards realistic team formation in social networks based on densest subgraphs"
    - "keep your friends close and your facebook friends closer a multiplex network approach to the analysis of offline and online social ties"
    - "locally estimating core numbers"
    - "geo social group queries with minimum acquaintance constraint"
    - "preventing unraveling in social networks gets harder"
    - "parameterized complexity of the anchored k core problem for directed graphs"
    - "community detection in networks with node attributes"
    - "mining attribute structure correlated patterns in large attributed graphs"

Connected through hyperedge HE-4 (induced by source paper "fast hierarchy construction for dense subgraphs"):
  with members:
    - "locally estimating core numbers"
    - "evolution of the internet k dense structure"
    - "truss decomposition in massive networks"
    - "social structure of facebook networks"
    - "a model of internet topology using k shell decomposition"
    - "k core decomposition of internet graphs hierarchies self similarity and measurement biases"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-5 (induced by source paper "a fast order based approach for core maintenance"):
  with members:
    - "truss decomposition in massive networks"
    - "an o m algorithm for cores decomposition of networks"

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)