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 "rambo repeated and merged bloom filter for multiple set membership testing msmt in sub linear time"):
  Abstract: Approximate set membership is a common problem with wide applications in databases, networking, and search. Given a set S and a query q, the task is to determine whether q in S. The Bloom Filter (BF) is a popular data structure for approximate membership testing due to its simplicity. In particular, a BF consists of a bit array that can be incrementally updated. A related problem concerning this paper is the Multiple Set Membership Testing (MSMT) problem. Here we are given K different sets, and for any given query q the goal is the find all of the sets containing the query element. Trivially, a multiple set membership instance can be reduced to K membership testing instances, each with the same q, leading to O(K) query time. A simple array of Bloom Filters can achieve that. In this paper, we show the first non-trivial data-structure for streaming keys, RAMBO (Repeated And Merged Bloom Filter) that achieves expected O(sqrt(K) logK) query time with an additional worst case memory cost factor of O(logK) than the array of Bloom Filters. The proposed data-structure is simply a count-min sketch arrangement of Bloom Filters and retains all its favorable properties. We replace the addition operation with a set union and the minimum operation with a set intersection during estimation.

Includes members:
  Member "extreme classification in log memory":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compressing gradient optimizers via count sketches")
  Member "beating countsketch for heavy hitters in insertion streams":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "data streams with bounded deletions")

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)