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: "set membership with non adaptive bit probes"
  Abstract: We consider the non-adaptive bit-probe complexity of the set membership problem, where a set S of size at most n from a universe of size m is to be represented as a short bit vector in order to answer membership queries of the form "Is x in S?" by non-adaptively probing the bit vector at t places. Let s_N(m,n,t) be the minimum number of bits of storage needed for such a scheme. In this work, we show existence of non-adaptive and adaptive schemes for a range of t that improves an upper bound of Buhrman, Miltersen, Radhakrishnan and Srinivasan (2002) on s_N(m,n,t). For three non-adaptive probes, we improve the previous best lower bound on s_N(m,n,3) by Alon and Feige (2009).

Connected through hyperedge HE-1 (induced by source paper "tight static lower bounds for non adaptive data structures"):
  with members:
    - "static data structure lower bounds imply rigidity"
    - "crossing the logarithmic barrier for dynamic boolean data structure lower bounds"
    - "amortized dynamic cell probe lower bounds from four party communication"
    - "set membership with a few bit probes"
    - "the cell probe complexity of dynamic range counting"
    - "lower bounds on near neighbor search via metric expansion"
    - "the power of simple tabulation hashing"
    - "linear probing with constant independence"

Connected through hyperedge HE-2 (induced by source paper "local decode and update for big data compression"):
  with members:
    - "how to store a random walk"
    - "compressed data structures for dynamic sequences"
    - "set membership with a few bit probes"
    - "bounds on the parameters of locally recoverable codes"
    - "a family of optimal locally recoverable codes"
    - "update efficiency and local repairability limits for capacity approaching codes"
    - "on locally decodable source coding"
    - "a simple online competitive adaptation of lempel ziv compression with efficient random access support"

Connected through hyperedge HE-3 (induced by source paper "a two query adaptive bitprobe scheme storing five elements"):
  with members:
    - "an improved scheme in the two query adaptive bitprobe model"
    - "set membership with a few bit probes"

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)