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: "fast random integer generation in an interval"
  Abstract: In simulations, probabilistic algorithms, and statistical tests, we often generate random integers in an interval (e.g., [0,s)). For example, random integers in an interval are essential to the Fisher-Yates random shuffle. Consequently, popular languages such as Java, Python, C++, Swift and Go include ranged random integer generation functions as part of their runtime libraries.#R##N##R##N#Pseudo-random values are usually generated in words of a fixed number of bits (e.g., 32b, 64b) using algorithms such as a linear congruential generator. We need functions to convert such random words to random integers in an interval ([0,s)) without introducing statistical biases. The standard functions in programming languages such as Java involve integer divisions. Unfortunately, division instructions are relatively expensive. We review an unbiased function to generate ranged integers from a source of random words that avoids integer divisions with high probability. To establish the practical usefulness of the approach, we show that this algorithm can multiply the speed of unbiased random shuffling on x64 processors. Our proposed approach has been adopted by the Go language for its implementation of the shuffle function.

Connected through hyperedge HE-1 (induced by source paper "probminhash a class of locality sensitive hash algorithms for the probability jaccard similarity"):
  with members:
    - "bagminhash minwise hashing algorithm for weighted sets"
    - "maximally consistent sampling and the jaccard index of probability distributions"
    - "fast similarity sketching"
    - "optimal densification for fast and accurate minwise hashing"
    - "superminhash a new minwise hashing algorithm for jaccard similarity estimation"
    - "b bit minwise hashing"

Connected through hyperedge HE-2 (induced by source paper "xor filters faster and smaller than bloom and cuckoo filters"):
  with members:
    - "regular and almost universal hashing an efficient implementation"
    - "bloofi multidimensional bloom filters"
    - "bloomier filters a second look"
    - "an optimal bloom filter replacement based on matrix solving"

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)