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: "effect of gromov hyperbolicity parameter on cuts and expansions in graphs and some algorithmic implications"
  Abstract: \(\delta \)-Hyperbolic graphs, originally conceived by Gromov (Essays in group theory. 1987), occur often in many network applications; for fixed \(\delta \), such graphs are simply called hyperbolic graphs and include non-trivial interesting classes of “non-expander” graphs. The main motivation of this paper is to investigate the effect of the hyperbolicity measure \(\delta \) on expansion and cut-size bounds on graphs (here \(\delta \) need not be a constant), and the asymptotic ranges of \(\delta \) for which these results may provide improved approximation algorithms for related combinatorial problems. To this effect, we provide constructive bounds on node expansions for \(\delta \)-hyperbolic graphs as a function of \(\delta \), and show that many witnesses (subsets of nodes) for such expansions can be computed efficiently even if the witnesses are required to be nested or sufficiently distinct from each other. To the best of our knowledge, these are the first such constructive bounds proven. We also show how to find a large family of s–t cuts with relatively small number of cut-edges when s and t are sufficiently far apart. We then provide algorithmic consequences of these bounds and their related proof techniques for two problems for \(\delta \)-hyperbolic graphs (where \(\delta \) is a function f of the number of nodes, the exact nature of growth of f being dependent on the particular problem considered).

Connected through hyperedge HE-1 (induced by source paper "fast approximation and exact computation of negative curvature parameters of graphs"):
  with members:
    - "fully polynomial fpt algorithms for some classes of bounded clique width graphs"
    - "when can graph hyperbolicity be computed in linear time"
    - "core congestion is inherent in hyperbolic networks"
    - "an improved combinatorial algorithm for boolean matrix multiplication"
    - "into the square on the complexity of quadratic time solvable problems"
    - "computing the gromov hyperbolicity of a discrete metric space"

Connected through hyperedge HE-2 (induced by source paper "why did the shape of your network change on detecting network anomalies via non local curvatures"):
  with members:
    - "fast approximation and exact computation of negative curvature parameters of graphs"
    - "forman ricci flow for change detection in large dynamic data sets"
    - "computing the gromov hyperbolicity of a discrete metric space"
    - "euclidean versus hyperbolic congestion in idealized versus experimental 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.CC(Computational Complexity)