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 computation of empirically tight bounds for the diameter of massive graphs"
  Abstract: The diameter of a graph is among its most basic parameters. Since a few years, it moreover became a key issue to compute it for massive graphs in the context of complex network analysis. However, known algorithms, including the ones producing approximate values, have too high a time and/or space complexity to be used in such cases. We propose here a new approach relying on very simple and fast algorithms that compute (upper and lower) bounds for the diameter. We show empirically that, on various real-world cases representative of complex networks studied in the literature, the obtained bounds are very tight (and even equal in some cases). This leads to rigorous and very accurate estimations of the actual diameter in cases which were previously untractable in practice.

Connected through hyperedge HE-1 (induced by source paper "domain specialized cache management for graph analytics"):
  with members:
    - "a closer look at lightweight graph reordering"
    - "low latency graph streaming using compressed purely functional trees"
    - "the gap benchmark suite"
    - "graphlab a new framework for parallel machine learning"

Connected through hyperedge HE-2 (induced by source paper "a closer look at lightweight graph reordering"):
  with members:
    - "domain specialized cache management for graph analytics"
    - "the gap benchmark suite"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "graphlab a new framework for parallel machine learning"

Connected through hyperedge HE-3 (induced by source paper "tight approximation algorithms for bichromatic graph diameter and related problems"):
  with members:
    - "towards tight approximation bounds for graph diameter and eccentricities"
    - "on pairwise spanners"

Connected through hyperedge HE-4 (induced by source paper "revisiting radius diameter and all eccentricity computation in graphs through certificates"):
  with members:
    - "an axiomatic and an average case analysis of algorithms and heuristics for metric properties of graphs"
    - "into the square on the complexity of quadratic time solvable problems"
    - "analytical approach to parallel repetition"
    - "four degrees of separation"

Connected through hyperedge HE-5 (induced by source paper "an axiomatic and an average case analysis of algorithms and heuristics for metric properties of graphs"):
  with members:
    - "computing top k closeness centrality faster in unweighted graphs"
    - "kadabra is an adaptive algorithm for betweenness via random approximation"
    - "snap a general purpose network analysis and graph mining library"
    - "fast and simple computation of top k closeness centralities"
    - "algorithmic complexity of power law networks"
    - "approximation and fixed parameter subquadratic algorithms for radius and diameter"
    - "networkit an interactive tool suite for high performance network analysis"
    - "into the square on the complexity of quadratic time solvable problems"

Connected through hyperedge HE-6 (induced by source paper "parallel wavelet tree construction"):
  with members:
    - "parallelism in randomized incremental algorithms"
    - "parallel local graph clustering"
    - "sorting with asymmetric read and write costs"
    - "efficient algorithms with asymmetric read and write costs"
    - "wavelet trees meet suffix trees"
    - "counting triangles in massive graphs with mapreduce"
    - "parallel graph decompositions using random shifts"
    - "skew strikes back new developments in the theory of join algorithms"

Connected through hyperedge HE-7 (induced by source paper "computing classic closeness centrality at scale"):
  with members:
    - "all distances sketches revisited hip estimators for massive graphs analysis"
    - "axioms for centrality"
    - "in core computation of geometric centralities with hyperball a hundred billion nodes and beyond"
    - "defining and evaluating network communities based on ground truth"
    - "four degrees of separation"
    - "robustness of social networks comparative results based on distance distributions"
    - "hyperanf approximating the neighbourhood function of very large graphs on a budget"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-8 (induced by source paper "networkit a tool suite for large scale complex network analysis"):
  with members:
    - "querying probabilistic neighborhoods in spatial data sets efficiently"
    - "engineering parallel algorithms for community detection in massive networks"
    - "snap a general purpose network analysis and graph mining library"
    - "structure preserving sparsification methods for social networks"
    - "fully dynamic approximation of betweenness centrality"
    - "approximating betweenness centrality in large evolving networks"
    - "social structure of facebook networks"
    - "the similarity between stochastic kronecker and chung lu graph models"

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)