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: "a minimal variance estimator for the cardinality of big data set intersection"
  Abstract: In recent years there has been a growing interest in developing "streaming algorithms" for efficient processing and querying of continuous data streams. These algorithms seek to provide accurate results while minimizing the required storage and the processing time, at the price of a small inaccuracy in their output. A fundamental query of interest is the intersection size of two big data streams. This problem arises in many different application areas, such as network monitoring, database systems, data integration and information retrieval. In this paper we develop a new algorithm for this problem, based on the Maximum Likelihood (ML) method. We show that this algorithm outperforms all known schemes and that it asymptotically achieves the optimal variance.

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"
    - "consistent weighted sampling made fast small and easy"
    - "finding associations and computing similarity via biased pair sampling"

Connected through hyperedge HE-2 (induced by source paper "gb kmv an augmented kmv sketch for approximate containment similarity search"):
  with members:
    - "silkmoth an efficient method for finding related sets with maximum matching constraints"
    - "fast similarity sketching"
    - "set containment join revisited"
    - "lsh ensemble internet scale domain search"
    - "set similarity search beyond minhash"

Connected through hyperedge HE-3 (induced by source paper "new cardinality estimation methods for hyperloglog sketches"):
  with members:
    - "new cardinality estimation algorithms for hyperloglog sketches"
    - "a framework for estimating stream expression cardinalities"
    - "hashing for similarity search a survey"
    - "all distances sketches revisited hip estimators for massive graphs analysis"

Connected through hyperedge HE-4 (induced by source paper "new cardinality estimation algorithms for hyperloglog sketches"):
  with members:
    - "a framework for estimating stream expression cardinalities"
    - "hashing for similarity search a survey"
    - "all distances sketches revisited hip estimators for massive graphs analysis"

Connected through hyperedge HE-5 (induced by source paper "efficient and private approximations of distributed databases calculations"):
  with members:
    - "parallel correctness and transferability for conjunctive queries"
    - "when distributed computation is communication expensive"
    - "calibrating data to sensitivity in private data analysis"
    - "what you can do with coordinated samples"
    - "coordinated weighted sampling for estimating aggregates over multiple weight assignments"
    - "explicit learning curves for transduction and application to clustering and compression algorithms"

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)