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: "bagminhash minwise hashing algorithm for weighted sets"
  Abstract: Minwise hashing has become a standard tool to calculate signatures which allow direct estimation of Jaccard similarities. While very efficient algorithms already exist for the unweighted case, the calculation of signatures for weighted sets is still a time consuming task. BagMinHash is a new algorithm that can be orders of magnitude faster than current state of the art without any particular restrictions or assumptions on weights or data dimensionality. Applied to the special case of unweighted sets, it represents the first efficient algorithm producing independent signature components. A series of tests finally verifies the new algorithm and also reveals limitations of other approaches published in the recent past.

Connected through hyperedge HE-1 (induced by source paper "probminhash a class of locality sensitive hash algorithms for the probability jaccard similarity"):
  with members:
    - "fast random integer generation in an interval"
    - "maximally consistent sampling and the jaccard index of probability distributions"
    - "fast similarity sketching"
    - "optimal densification for fast and accurate minwise hashing"
    - "superminhash a new minwise hashing algorithm for jaccard similarity estimation"
    - "b bit minwise hashing"

Connected through hyperedge HE-2 (induced by source paper "a memory efficient sketch method for estimating high similarities in streaming sets"):
  with members:
    - "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"
    - "finding associations and computing similarity via biased pair sampling"

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)