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: "efficient generation e close to g n p and generalizations"
  Abstract: We give an efficient algorithm to generate a graph from a distribution $\epsilon$-close to $G(n,p)$, in the sense of total variation distance. In particular, if $p$ is represented with $O(\log n)$-bit accuracy, then, with high probability, the running time is linear in the expected number of edges of the output graph (up to poly-logarithmic factors). All our running times include the complexity of the arithmetic involved in the corresponding algorithms. Previous standard methods for exact $G(n,p)$ sampling (see e.g. Batagelj and Brandes, 2005) achieve similar running times, however, under the assumption that performing real number arithmetic with arbitrary accuracy takes constant time. We note that the actual accuracy required by these methods is O(n)-bit per step, which results in quadratic running times. #R##N#The main idea of our $G(n,p)$ generation algorithm is a Metropolis Markov chain to sample $\epsilon$-close from the binomial distribution. This is a new method for sampling from the binomial distribution: it is of separate interest and may find other useful applications. Our analysis accounts for all necessary bit-accuracy and arithmetic, and our running times are comparable to known methods for exact binomial sampling. #R##N#We further obtain efficient generation algorithms for random graphs with given arbitrary degree distributions, Inhomogeneous Random Graphs when the kernel function is the inner product, and Stochastic Kronecker Graphs. To the best our knowledge, our work can be viewed as the first effort to simulate efficient generation of graphs from classical random graph models, while taking into account implementational considerations as fundamental computational aspects, and quantifying the tradeoff between accuracy and running time in a way that can be useful in practice.

Connected through hyperedge HE-1 (induced by source paper "recent advances in scalable network generation"):
  with members:
    - "efficiently generating geometric inhomogeneous and hyperbolic random graphs"
    - "exploring randomly wired neural networks for image recognition"
    - "parallel weighted random sampling"
    - "linear work generation of r mat graphs"
    - "egbter capturing degree distribution clustering coefficients and community structure in a single random graph model"
    - "multiscale planar graph generation"
    - "parallel and i o efficient randomisation of massive networks using global curveball trades"
    - "efficient parallel random sampling vectorized cache efficient and online"

Connected through hyperedge HE-2 (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"
    - "optimal discrete uniform generation from coin flips and applications"

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)