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: "in core computation of geometric centralities with hyperball a hundred billion nodes and beyond"
  Abstract: Given a social network, which of its nodes are more central? This question has been asked many times in sociology, psychology and computer science, and a whole plethora of centrality measures (a.k.a. centrality indices, or rankings) were proposed to account for the importance of the nodes of a network. In this paper, we approach the problem of computing geometric centralities, such as closeness and harmonic centrality, on very large graphs; traditionally this task requires an all-pairs shortest-path computation in the exact case, or a number of breadth-first traversals for approximated computations, but these techniques yield very weak statistical guarantees on highly disconnected graphs. We rather assume that the graph is accessed in a semi-streaming fashion, that is, that adjacency lists are scanned almost sequentially, and that a very small amount of memory (in the order of a dozen bytes) per node is available in core memory. We leverage the newly discovered algorithms based on HyperLogLog counters, making it possible to approximate a number of geometric centralities at a very high speed and with high accuracy. While the application of similar algorithms for the approximation of closeness was attempted in the MapReduce framework, our exploitation of HyperLogLog counters reduces exponentially the memory footprint, paving the way for in-core processing of networks with a hundred billion nodes using "just" 2TiB of RAM. Moreover, the computations we describe are inherently parallelizable, and scale linearly with the number of available cores.

Connected through hyperedge HE-1 (induced by source paper "computing top k closeness centrality faster in unweighted graphs"):
  with members:
    - "a note on the complexity of computing the number of reachable vertices in a digraph"
    - "fast and simple computation of top k closeness centralities"
    - "approximation and fixed parameter subquadratic algorithms for radius and diameter"
    - "average distance queries through weighted samples in graphs and metric spaces high scalability with tight statistical guarantees"
    - "powers of tensors and fast matrix multiplication"
    - "networkit an interactive tool suite for high performance network analysis"
    - "networkit a tool suite for large scale complex network analysis"
    - "popular conjectures imply strong lower bounds for dynamic problems"

Connected through hyperedge HE-2 (induced by source paper "active betweenness cardinality algorithms and applications"):
  with members:
    - "all distances sketches revisited hip estimators for massive graphs analysis"

Connected through hyperedge HE-3 (induced by source paper "bubing massive crawling for the masses"):
  with members:
    - "a brief history of web crawlers"
    - "axioms for centrality"

Connected through hyperedge HE-4 (induced by source paper "timed influence computation and maximization"):
  with members:
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "all distances sketches revisited hip estimators for massive graphs analysis"
    - "axioms for centrality"
    - "trace complexity of network inference"
    - "scalable influence estimation in continuous time diffusion networks"
    - "maximizing social influence in nearly optimal time"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-5 (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"
    - "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"
    - "engineering a scalable high quality graph partitioner"

Connected through hyperedge HE-6 (induced by source paper "alpha current flow betweenness centrality"):
  with members:
    - "a note on the pagerank of undirected graphs"
    - "axioms for centrality"

Connected through hyperedge HE-7 (induced by source paper "axioms for centrality"):
  with members:
    - "four degrees of separation"

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)