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: "better size estimation for sparse matrix products"
  Abstract: We consider the problem of doing fast and reliable estimation of the number of non-zero entries in a sparse boolean matrix product. This problem has applications in databases and computer algebra. Let n denote the total number of non-zero entries in the input matrices. We show how to compute a 1 +- epsilon approximation (with small probability of error) in expected time O(n) for any epsilon > 4/\sqrt[4]{z}. The previously best estimation algorithm, due to Cohen (JCSS 1997), uses time O(n/epsilon^2). We also present a variant using O(sort(n)) I/Os in expectation in the cache-oblivious model. In contrast to these results, the currently best algorithms for computing a sparse boolean matrix product use time omega(n^{4/3}) (resp. omega(n^{4/3}/B) I/Os), even if the result matrix has only z=O(n) nonzero entries. Our algorithm combines the size estimation technique of Bar-Yossef et al. (RANDOM 2002) with a particular class of pairwise independent hash functions that allows the sketch of a set of the form A x C to be computed in expected time O(|A|+|C|) and O(sort(|A|+|C|)) I/Os. We then describe how sampling can be used to maintain (independent) sketches of matrices that allow estimation to be performed in time o(n) if z is sufficiently large. This gives a simpler alternative to the sketching technique of Ganguly et al. (PODS 2005), and matches a space lower bound shown in that paper. Finally, we present experiments on real-world data sets that show the accuracy of both our methods to be significantly better than the worst-case analysis predicts.

Connected through hyperedge HE-1 (induced by source paper "a systematic survey of general sparse matrix matrix multiplication"):
  with members:
    - "high performance sparse matrix matrix products on intel knl and multicore architectures"
    - "sparse matrix matrix multiplication on multilevel memory architectures algorithms and experiments"
    - "multi threaded sparse matrix matrix multiplication for many core and gpu architectures"
    - "porting of the dbcsr library for sparse matrix matrix multiplications to intel xeon phi systems"
    - "static graph challenge subgraph isomorphism"
    - "a comparative study on exact triangle counting algorithms on the gpu"
    - "hypergraph partitioning for sparse matrix matrix multiplication"
    - "locality aware parallel block sparse matrix matrix multiplication using the chunks and tasks programming model"

Connected through hyperedge HE-2 (induced by source paper "distributed statistical estimation of matrix products with applications"):
  with members:
    - "distributed pcp theorems for hardness of approximation in p"
    - "worst case optimal algorithms for parallel query processing"
    - "distributed low rank approximation of implicit functions of a matrix"
    - "on the complexity of inner product similarity join"
    - "communication efficient distributed kernel principal component analysis"
    - "optimal principal component analysis in distributed and streaming models"
    - "sketching as a tool for numerical linear algebra"
    - "skew in parallel query processing"

Connected through hyperedge HE-3 (induced by source paper "a framework for general sparse matrix matrix multiplication on gpus and heterogeneous processors"):
  with members:
    - "speculative segmented sum for sparse matrix vector multiplication on heterogeneous processors"
    - "csr5 an efficient storage format for cross platform sparse matrix vector multiplication"
    - "the input output complexity of sparse matrix multiplication"
    - "performance evaluation of sparse matrix multiplication kernels on intel xeon phi"
    - "parallel sparse matrix matrix multiplication and indexing implementation and experiments"
    - "finding heaviest h subgraphs in real weighted graphs with applications"

Connected through hyperedge HE-4 (induced by source paper "join size estimation on boolean tensors of rdf data"):
  with members:
    - "a technique for sparql optional left outer join queries"
    - "fdb a query engine for factorised relational databases"

Connected through hyperedge HE-5 (induced by source paper "consistent subset sampling"):
  with members:
    - "on parallelizing matrix multiplication by the column row method"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"

Connected through hyperedge HE-6 (induced by source paper "on parallelizing matrix multiplication by the column row method"):
  with members:
    - "compressed matrix multiplication"
    - "on finding similar items in a stream of transactions"
    - "finding associations and computing similarity via biased pair sampling"

Connected through hyperedge HE-7 (induced by source paper "space round tradeoffs for mapreduce computations"):
  with members:
    - "sorting searching and simulation in the mapreduce framework"
    - "simulating parallel algorithms in the mapreduce framework with applications to parallel computational geometry"

Connected through hyperedge HE-8 (induced by source paper "compressed matrix multiplication"):
  with members:
    - "faster algorithms for rectangular matrix multiplication"
    - "deterministic algorithms for skewed matrix products"
    - "algorithmic and statistical perspectives on large scale data analysis"
    - "the power of simple tabulation hashing"
    - "approximate sparse recovery optimizing time and measurements"
    - "ams without 4 wise independence on product domains"

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)