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: "analysis of a bloom filter algorithm via the supermarket model"
  Abstract: This paper deals with the problem of identifying elephants in the Internet Traffic. The aim is to analyze a new adaptive algorithm based on a Bloom Filter. This algorithm uses a so-called min-rule which can be described as in the supermarket model. This model consists of joining the shortest queue among d queues selected at random in a large number of m queues. In case of equality, one of the shortest queues is chosen at random. An analysis of a simplified model gives an insight of the error generated by the algorithm on the estimation of the number of the elephants. The main conclusion is that, as m gets large, there is a deterministic limit for the empirical distribution of the filter counters. Limit theorems are proved and the limit is identified. It depends on key parameters. The condition for the algorithm to perform well is discussed. Theoretical results are validated by experiments on a traffic trace from France Telecom and by simulations.

Connected through hyperedge HE-1 (induced by source paper "streaming frequent items with timestamps and detecting large neighborhoods in graph streams"):
  with members:
    - "optimality of linear sketching under modular updates"
    - "independent sets in vertex arrival streams"
    - "tight space approximation tradeoff for the multi pass streaming set cover problem"
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "towards tight bounds for the streaming set cover problem"
    - "maximum matching in turnstile streams"
    - "semi streaming set cover"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"

Connected through hyperedge HE-2 (induced by source paper "an optimal algorithm for l1 heavy hitters in insertion streams and related problems"):
  with members:
    - "bptree an ell_2 heavy hitters algorithm using constant memory"
    - "new algorithms for heavy hitters in data streams"
    - "beating countsketch for heavy hitters in insertion streams"
    - "sample complexity for winner prediction in elections"
    - "medians and beyond new aggregation techniques for sensor networks"

Connected through hyperedge HE-3 (induced by source paper "beating countsketch for heavy hitters in insertion streams"):
  with members:
    - "bptree an ell_2 heavy hitters algorithm using constant memory"
    - "tracking the frequency moments at all times"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "approximating large frequency moments with pick and drop sampling"
    - "low rank approximation and regression in input sparsity time"
    - "improved concentration bounds for count sketch"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"

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.DM(Discrete Mathematics)