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: "computing hypergraph width measures exactly"
  Abstract: Hypergraph width measures are a class of hypergraph invariants important in studying the complexity of constraint satisfaction problems (CSPs). We present a general exact exponential algorithm for a large variety of these measures. A connection between these and tree decompositions is established. This enables us to almost seamlessly adapt the combinatorial and algorithmic results known for tree decompositions of graphs to the case of hypergraphs and obtain fast exact algorithms. #R##N#As a consequence, we provide algorithms which, given a hypergraph H on n vertices and m hyperedges, compute the generalized hypertree-width of H in time O*(2^n) and compute the fractional hypertree-width of H in time O(m*1.734601^n).

Connected through hyperedge HE-1 (induced by source paper "complexity analysis of general and fractional hypertree decompositions"):
  with members:
    - "hyperbench a benchmark and tool for hypergraphs and empirical findings"
    - "tree projections and constraint optimization problems fixed parameter tractability and parallel algorithms"
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "an analytical study of large sparql query logs"
    - "old techniques for new join algorithms a case study in rdf processing"
    - "general and fractional hypertree decompositions hard and easy cases"
    - "myhill nerode methods for hypergraphs"

Connected through hyperedge HE-2 (induced by source paper "potential maximal cliques parameterized by edge clique cover"):
  with members:
    - "hyperbench a benchmark and tool for hypergraphs and empirical findings"
    - "constraint solving via fractional edge covers"
    - "on the tractability of optimization problems on h graphs"
    - "ranked enumeration of minimal triangulations"
    - "positive instance driven dynamic programming for treewidth"
    - "on distance d independent set and other problems in graphs with few minimal separators"
    - "general and fractional hypertree decompositions hard and easy cases"
    - "on the number of minimal separators in graphs"

Connected through hyperedge HE-3 (induced by source paper "hyperbench a benchmark and tool for hypergraphs and empirical findings"):
  with members:
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "an analytical study of large sparql query logs"
    - "old techniques for new join algorithms a case study in rdf processing"
    - "xcsp3 an integrated format for benchmarking combinatorial constrained problems"
    - "general and fractional hypertree decompositions hard and easy cases"
    - "faq questions asked frequently"
    - "joins via geometric resolutions worst case and beyond"

Connected through hyperedge HE-4 (induced by source paper "general and fractional hypertree decompositions hard and easy cases"):
  with members:
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "an analytical study of large sparql query logs"
    - "old techniques for new join algorithms a case study in rdf processing"
    - "myhill nerode methods for hypergraphs"
    - "faq questions asked frequently"
    - "joins via geometric resolutions worst case and beyond"
    - "gym a multiround join algorithm in mapreduce"

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.CC(Computational Complexity)