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: "hyperanf approximating the neighbourhood function of very large graphs on a budget"
  Abstract: The neighbourhood function N(t) of a graph G gives, for each t, the number of pairs of nodes   such that y is reachable from x in less that t hops. The neighbourhood function provides a wealth of information about the graph (e.g., it easily allows one to compute its diameter), but it is very expensive to compute it exactly. Recently, the ANF algorithm (approximate neighbourhood function) has been proposed with the purpose of approximating NG(t) on large graphs. We describe a breakthrough improvement over ANF in terms of speed and scalability. Our algorithm, called HyperANF, uses the new HyperLogLog counters and combines them efficiently through broadword programming; our implementation uses overdecomposition to exploit multi-core parallelism. With HyperANF, for the first time we can compute in a few hours the neighbourhood function of graphs with billions of nodes with a small error and good confidence using a standard workstation. Then, we turn to the study of the distribution of the shortest paths between reachable nodes (that can be efficiently approximated by means of HyperANF), and discover the surprising fact that its index of dispersion provides a clear-cut characterisation of proper social networks vs. web graphs. We thus propose the spid (Shortest-Paths Index of Dispersion) of a graph as a new, informative statistics that is able to discriminate between the above two types of graphs. We believe this is the first proposal of a significant new non-local structural index for complex networks whose computation is highly scalable.

Connected through hyperedge HE-1 (induced by source paper "slim graph practical lossy graph compression for approximate graph processing storage and analytics"):
  with members:
    - "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"
    - "graph processing on fpgas taxonomy survey challenges"
    - "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "active access a mechanism for high performance distributed data centric computations"
    - "communication efficient jaccard similarity for high performance distributed genome comparisons"
    - "local computation algorithms for spanners"
    - "survey and taxonomy of lossless graph compression and space efficient graph representations"

Connected through hyperedge HE-2 (induced by source paper "dolha an efficient and exact data structure for streaming graphs"):
  with members:
    - "time constrained continuous subgraph search over streaming graphs"
    - "graphstream a tool for bridging the gap between complex systems and dynamic graphs"

Connected through hyperedge HE-3 (induced by source paper "aggregate graph statistics"):
  with members:
    - "asynchronous distributed execution of fixpoint based computational fields"
    - "four degrees of separation"

Connected through hyperedge HE-4 (induced by source paper "efficiently clustering very large attributed graphs"):
  with members:
    - "clustering attributed graphs models measures and methods"
    - "community detection in networks with node attributes"
    - "a classification for community discovery methods in complex networks"
    - "the anatomy of the facebook social graph"
    - "four degrees of separation"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-5 (induced by source paper "semi supervised learning on graphs through reach and distance diffusion"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "scalable facility location for massive graphs on pregel like systems"
    - "multi objective weighted sampling"
    - "reverse ranking by graph structure model and scalable algorithms"
    - "large scale distributed semi supervised learning using streaming approximation"
    - "deepwalk online learning of social representations"
    - "fast ppr scaling personalized pagerank estimation for large graphs"

Connected through hyperedge HE-6 (induced by source paper "fractality of massive graphs scalable analysis with sketch based box covering algorithm"):
  with members:
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "all distances sketches revisited hip estimators for massive graphs analysis"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "a model of internet topology using k shell decomposition"

Connected through hyperedge HE-7 (induced by source paper "reverse ranking by graph structure model and scalable algorithms"):
  with members:
    - "scalable facility location for massive graphs on pregel like systems"
    - "multi objective weighted sampling"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "timed influence computation and maximization"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "all distances sketches revisited hip estimators for massive graphs analysis"
    - "trace complexity of network inference"
    - "scalable influence estimation in continuous time diffusion networks"

Connected through hyperedge HE-8 (induced by source paper "scalable facility location for massive graphs on pregel like systems"):
  with members:
    - "all distances sketches revisited hip estimators for massive graphs analysis"
    - "greedy sequential maximal independent set and matching are parallel on average"
    - "densest subgraph in streaming and mapreduce"
    - "social content matching in mapreduce"
    - "parallel approximation algorithms for facility location problems"
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

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)