human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a new frequency estimation sketch for data streams"):
  Abstract: In data stream applications, one of the critical issues is to estimate the frequency of each item in the specific multiset. The multiset means that each item in this set can appear multiple times. The data streams in many applications are high-speed streams which contain massive data, such as real-time IP traffic, graph streams, web clicks and crawls, sensor database, and natural language processing (NLP) [2][6], etc. In these applications, the stream information needs to be recorded by the servers in real time. However, since the data streams in these applications are high-speed, the accurate recording and estimation of item frequencies is always impractical. An alternative approach for addressing this problem is to estimate the item frequencies based on probabilistic data structures, and this approach has been widely used in the high-speed data streams estimation [7][9]. Sketches is one of the typical probabilistic data structures, which are initially designed for the estimation of item frequencies in data streams [10][15]. At present, the sketches have been used in many different scenarios, such as sparse approximation in compressed sensing [16], natural language processing [17, 18], data graph [19, 20], and more [21]. In this paper, we mainly focus on the sketches used for frequency estimation.

Includes members:
  Member "a shifting bloom filter framework for set queries":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bloom filter variants for multiple sets a comparative assessment")
  Member "mining top k frequent itemsets through progressive sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive sampling for rapidly matching histograms")

Question: Please tell me which class the source paper 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)