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: "fast approximation and exact computation of negative curvature parameters of graphs"
  Abstract: In this paper, we study Gromov hyperbolicity and related parameters, that represent how close (locally) a metric space is to a tree from a metric point of view. The study of Gromov hyperbolicity for geodesic metric spaces can be reduced to the study of graph hyperbolicity. The main contribution of this paper is a new characterization of the hyperbolicity of graphs, via a new parameter which we call rooted insize. This characterization has algorithmic implications in the field of large-scale network analysis. A sharp estimate of graph hyperbolicity is useful, e.g., in embedding an undirected graph into hyperbolic space with minimum distortion (Verbeek and Suri, in Symposium on Computational Geometry, ACM, New York, 2014). The hyperbolicity of a graph can be computed in polynomial-time, however it is unlikely that it can be done in subcubic time. This makes this parameter difficult to compute or to approximate on large graphs. Using our new characterization of graph hyperbolicity, we provide a simple factor 8 approximation algorithm (with an additive constant 1) for computing the hyperbolicity of an n-vertex graph \(G=(V,E)\) in optimal time \(O(n^2)\) (assuming that the input is the distance matrix of the graph). This algorithm leads to constant factor approximations of other graph-parameters related to hyperbolicity (thinness, slimness, and insize). We also present the first efficient algorithms for exact computation of these parameters. All of our algorithms can be used to approximate the hyperbolicity of a geodesic metric space. We also show that a similar characterization of hyperbolicity holds for all geodesic metric spaces endowed with a geodesic spanning tree. Along the way, we prove that any complete geodesic metric space (X, d) has such a geodesic spanning tree.

Connected through hyperedge HE-1 (induced by source paper "eccentricity terrain of delta hyperbolic graphs"):
  with members:
    - "revisiting radius diameter and all eccentricity computation in graphs through certificates"
    - "core congestion is inherent in hyperbolic networks"
    - "metric tree like structures in real life networks an empirical study"
    - "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:
    - "effect of gromov hyperbolicity parameter on cuts and expansions in graphs and some algorithmic implications"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "fast approximation of centrality and distances in hyperbolic graphs"):
  with members:
    - "fast approximation algorithms for p centres in large delta hyperbolic graphs"
    - "core congestion is inherent in hyperbolic networks"
    - "into the square on the complexity of quadratic time solvable problems"

Connected through hyperedge HE-4 (induced by source paper "slimness of graphs"):
  with members:
    - "core congestion is inherent in hyperbolic networks"
    - "tree decompositions and social graphs"
    - "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.DS(Data Structures and Algorithms)