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 "optimal approximate sampling from discrete probability distributions"):
  Abstract: This paper addresses a fundamental problem in random variate generation: given access to a random source that emits a stream of independent fair bits, what is the most accurate and entropy-efficient algorithm for sampling from a discrete probability distribution (p₁, …, pₙ), where the output distribution (p₁, …, pₙ) of the sampling algorithm can be specified with a given level of bit precision? We present a theoretical framework for formulating this problem and provide new techniques for finding sampling algorithms that are optimal both statistically (in the sense of sampling accuracy) and information-theoretically (in the sense of entropy consumption). We leverage these results to build a system that, for a broad family of measures of statistical accuracy, delivers a sampling algorithm whose expected entropy usage is minimal among those that induce the same distribution (i.e., is “entropy-optimal”) and whose output distribution (p₁, …, pₙ) is a closest approximation to the target distribution (p₁, …, pₙ) among all entropy-optimal sampling algorithms that operate within the specified precision budget. This optimal approximate sampler is also a closer approximation than any (possibly entropy-suboptimal) sampler that consumes a bounded amount of entropy with the specified precision, a class which includes floating-point implementations of inversion sampling and related methods found in many standard software libraries. We evaluate the accuracy, entropy consumption, precision requirements, and wall-clock runtime of our optimal approximate sampling algorithms on a broad set of probability distributions, demonstrating the ways that they are superior to existing approximate samplers and establishing that they often consume significantly fewer resources than are needed by exact samplers.

Includes members:
  Member "bayesian synthesis of probabilistic programs for automatic data modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bayesian causal inference via probabilistic program synthesis")
  Member "coalgebraic tools for randomness conserving protocols":
    also connected through other hyperedges:
      (none)
  Member "probabilistic data analysis with probabilistic programming":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bayesian synthesis of probabilistic programs for automatic data modeling")
  Member "sampling with arbitrary precision":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mergeshuffle a very fast parallel random permutation algorithm")
  Member "building fast bayesian computing machines out of intentionally stochastic digital parts":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning machines implemented on non deterministic hardware")
  Member "optimal discrete uniform generation from coin flips and applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a model counter s guide to probabilistic systems")
  Member "efficient generation e close to g n p and generalizations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "recent advances in scalable network generation")

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)