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: "optimal discrete uniform generation from coin flips and applications"
  Abstract: This article introduces an algorithm to draw random discrete uniform variables within a given range of size n from a source of random bits. The algorithm aims to be simple to implement and optimal both with regards to the amount of random bits consumed, and from a computational perspective---allowing for faster and more efficient Monte-Carlo simulations in computational physics and biology. I also provide a detailed analysis of the number of bits that are spent per variate, and offer some extensions and applications, in particular to the optimal random generation of permutations.

Connected through hyperedge HE-1 (induced by source paper "optimal approximate sampling from discrete probability distributions"):
  with members:
    - "bayesian synthesis of probabilistic programs for automatic data modeling"
    - "coalgebraic tools for randomness conserving protocols"
    - "probabilistic data analysis with probabilistic programming"
    - "sampling with arbitrary precision"
    - "building fast bayesian computing machines out of intentionally stochastic digital parts"
    - "efficient generation e close to g n p and generalizations"

Connected through hyperedge HE-2 (induced by source paper "a model counter s guide to probabilistic systems"):
  with members:
    - "sequential circuits from regular expressions revisited"
    - "learning task specifications from demonstrations"
    - "a storm is coming a modern probabilistic model checker"
    - "a scalable approximate model counter"

Connected through hyperedge HE-3 (induced by source paper "bagminhash minwise hashing algorithm for weighted sets"):
  with members:
    - "topic modeling of public repositories at scale using names in source code"
    - "fast similarity sketching"
    - "optimal densification for fast and accurate minwise hashing"
    - "improved consistent weighted sampling revisited"
    - "superminhash a new minwise hashing algorithm for jaccard similarity estimation"
    - "ssh sketch shingle hash for indexing massive scale time series"
    - "linearized gmm kernels and normalized random fourier features"
    - "consistent weighted sampling made fast small and easy"

Connected through hyperedge HE-4 (induced by source paper "superminhash a new minwise hashing algorithm for jaccard similarity estimation"):
  with members:
    - "fast similarity sketching"
    - "optimal densification for fast and accurate minwise hashing"

Connected through hyperedge HE-5 (induced by source paper "mergeshuffle a very fast parallel random permutation algorithm"):
  with members:
    - "sampling with arbitrary precision"
    - "efficient random sampling of binary and unary binary trees via holonomic equations"

Connected through hyperedge HE-6 (induced by source paper "analytic samplers and the combinatorial rejection method"):
  with members:
    - "multi dimensional boltzmann sampling of languages"

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)