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: "finding associations and computing similarity via biased pair sampling"
  Abstract: This version is ***superseded*** by a full version that can be found at this http URL, which contains stronger theoretical results and fixes a mistake in the reporting of experiments. #R##N#Abstract: Sampling-based methods have previously been proposed for the problem of finding interesting associations in data, even for low-support items. While these methods do not guarantee precise results, they can be vastly more efficient than approaches that rely on exact counting. However, for many similarity measures no such methods have been known. In this paper we show how a wide variety of measures can be supported by a simple biased sampling method. The method also extends to find high-confidence association rules. We demonstrate theoretically that our method is superior to exact methods when the threshold for "interesting similarity/confidence" is above the average pairwise similarity/confidence, and the average support is not too low. Our method is particularly good when transactions contain many items. We confirm in experiments on standard association mining benchmarks that this gives a significant speedup on real data sets (sometimes much larger than the theoretical guarantees). Reductions in computation time of over an order of magnitude, and significant savings in space, are observed.

Connected through hyperedge HE-1 (induced by source paper "a memory efficient sketch method for estimating high similarities in streaming sets"):
  with members:
    - "bagminhash minwise hashing algorithm for weighted sets"
    - "hyperminhash jaccard index sketching in loglog space"
    - "optimal densification for fast and accurate minwise hashing"
    - "a minimal variance estimator for the cardinality of big data set intersection"
    - "consistent weighted sampling made fast small and easy"

Connected through hyperedge HE-2 (induced by source paper "diamond sampling for approximate maximum all pairs dot product mad search"):
  with members:
    - "image based recommendations on styles and substitutes"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "compressed matrix multiplication"
    - "low rank matrix valued chernoff bounds and approximate matrix multiplication"
    - "on sparse representations of linear operators and the approximation of matrix products"

Connected through hyperedge HE-3 (induced by source paper "on parallelizing matrix multiplication by the column row method"):
  with members:
    - "compressed matrix multiplication"
    - "better size estimation for sparse matrix products"
    - "on finding similar items in a stream of transactions"

Connected through hyperedge HE-4 (induced by source paper "dimension independent similarity computation"):
  with members:
    - "dimension independent matrix square using mapreduce"
    - "complexity measures for map reduce and comparison to parallel computing"

Connected through hyperedge HE-5 (induced by source paper "deterministic algorithms for skewed matrix products"):
  with members:
    - "compressed matrix multiplication"

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)