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: "an axiomatic and an average case analysis of algorithms and heuristics for metric properties of graphs"
  Abstract: In recent years, researchers proposed several algorithms that compute metric quantities of real-world complex networks, and that are very efficient in practice, although there is no worst-case guarantee. #R##N#In this work, we propose an axiomatic framework to analyze the performances of these algorithms, by proving that they are efficient on the class of graphs satisfying certain axioms. Furthermore, we prove that the axioms are verified asymptotically almost surely by several probabilistic models that generate power law random graphs, such as the In recent years, researchers proposed several algorithms that compute metric quantities of real-world complex networks, and that are very efficient in practice, although there is no worst-case guarantee. #R##N#In this work, we propose an axiomatic framework to analyze the performances of these algorithms, by proving that they are efficient on the class of graphs satisfying certain properties. Furthermore, we prove that these properties are verified asymptotically almost surely by several probabilistic models that generate power law random graphs, such as the Configuration Model, the Chung-Lu model, and the Norros-Reittu model. Thus, our results imply average-case analyses in these models. #R##N#For example, in our framework, existing algorithms can compute the diameter and the radius of a graph in subquadratic time, and sometimes even in time $n^{1+o(1)}$. Moreover, in some regimes, it is possible to compute the $k$ most central vertices according to closeness centrality in subquadratic time, and to design a distance oracle with sublinear query time and subquadratic space occupancy. #R##N#In the worst case, it is impossible to obtain comparable results for any of these problems, unless widely-believed conjectures are false.

Connected through hyperedge HE-1 (induced by source paper "parameterized complexity of diameter"):
  with members:
    - "towards improving brandes algorithm for betweenness centrality"
    - "towards tight approximation bounds for graph diameter and eccentricities"
    - "data reduction for maximum matching on real world graphs theory and experiments"
    - "multivariate analysis of orthogonal range searching and graph distances parameterized by treewidth"
    - "efficient and adaptive parameterized algorithms on modular decompositions"
    - "parameterized aspects of triangle enumeration"
    - "fully polynomial fpt algorithms for some classes of bounded clique width graphs"
    - "voronoi diagrams on planar graphs and computing the diameter in deterministic tilde o n 5 3 time"

Connected through hyperedge HE-2 (induced by source paper "revisiting radius diameter and all eccentricity computation in graphs through certificates"):
  with members:
    - "into the square on the complexity of quadratic time solvable problems"
    - "analytical approach to parallel repetition"
    - "four degrees of separation"
    - "fast computation of empirically tight bounds for the diameter of massive graphs"

Connected through hyperedge HE-3 (induced by source paper "pruning based distance sketches with provable guarantees on random graphs"):
  with members:
    - "algorithmic and hardness results for the hub labeling problem"
    - "on the complexity of hub labeling"
    - "sublinear distance labeling for sparse graphs"
    - "route planning in transportation networks"
    - "hop doubling label indexing for point to point distance querying on scale free networks"
    - "a scalable generative graph model with community structure"
    - "separating hierarchical and general hub labelings"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"

Connected through hyperedge HE-4 (induced by source paper "distance labelings on random power law graphs"):
  with members:
    - "algorithmic and hardness results for the hub labeling problem"
    - "on the complexity of hub labeling"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"
    - "approximate distance oracles with improved preprocessing time"
    - "linear space approximate distance oracles for planar bounded genus and minor free graphs"

Connected through hyperedge HE-5 (induced by source paper "kadabra is an adaptive algorithm for betweenness via random approximation"):
  with members:
    - "dynamic merging of frontiers for accelerating the evaluation of betweenness centrality"
    - "computing top k closeness centrality faster in unweighted graphs"
    - "metropolis hastings algorithms for estimating betweenness centrality in large networks"
    - "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"
    - "bubing massive crawling for the masses"
    - "fully dynamic approximation of betweenness centrality"
    - "networkit an interactive tool suite for high performance network analysis"
    - "into the square on the complexity of quadratic time solvable problems"

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)