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: "consistent weighted sampling made fast small and easy"
  Abstract: Document sketching using Jaccard similarity has been a workable effective technique in reducing near-duplicates in Web page and image search results, and has also proven useful in file system synchronization, compression and learning applications. #R##N#Min-wise sampling can be used to derive an unbiased estimator for Jaccard similarity and taking a few hundred independent consistent samples leads to compact sketches which provide good estimates of pairwise-similarity. Subsequent works extended this technique to weighted sets and show how to produce samples with only a constant number of hash evaluations for any element, independent of its weight. Another improvement by Li et al. shows how to speedup sketch computations by computing many (near-)independent samples in one shot. Unfortunately this latter improvement works only for the unweighted case. #R##N#In this paper we give a simple, fast and accurate procedure which reduces weighted sets to unweighted sets with small impact on the Jaccard similarity. This leads to compact sketches consisting of many (near-)independent weighted samples which can be computed with just a small constant number of hash function evaluations per weighted element. The size of the produced unweighted set is furthermore a tunable parameter which enables us to run the unweighted scheme of Li et al. in the regime where it is most efficient. Even when the sets involved are unweighted, our approach gives a simple solution to the densification problem that other works attempted to address. #R##N#Unlike previously known schemes, ours does not result in an unbiased estimator. However, we prove that the bias introduced by our reduction is negligible and that the standard deviation is comparable to the unweighted case. We also empirically evaluate our scheme and show that it gives significant gains in computational efficiency, without any measurable loss in accuracy.

Connected through hyperedge HE-1 (induced by source paper "topic discovery in massive text corpora based on min hashing"):
  with members:
    - "saberlda sparsity aware learning of topic models on gpus"
    - "sampled weighted min hashing for large scale topic mining"
    - "a scalable asynchronous distributed algorithm for topic modeling"
    - "how many topics stability analysis for topic models"
    - "stochastic collapsed variational bayesian inference for latent dirichlet allocation"
    - "sparse stochastic inference for latent dirichlet allocation"

Connected through hyperedge HE-2 (induced by source paper "a memory efficient sketch method for estimating high similarities in streaming sets"):
  with members:
    - "bagminhash minwise hashing algorithm for weighted sets"
    - "hyperminhash jaccard index sketching in loglog space"
    - "optimal densification for fast and accurate minwise hashing"
    - "a minimal variance estimator for the cardinality of big data set intersection"
    - "finding associations and computing similarity via biased pair sampling"

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"
    - "optimal discrete uniform generation from coin flips and applications"

Connected through hyperedge HE-4 (induced by source paper "a review for weighted minhash algorithms"):
  with members:
    - "improved consistent weighted sampling revisited"
    - "bayesian locality sensitive hashing for fast similarity search"

Connected through hyperedge HE-5 (induced by source paper "probabilistic blocking with an application to the syrian conflict"):
  with members:
    - "a comparison of blocking methods for record linkage"

Connected through hyperedge HE-6 (induced by source paper "optimal densification for fast and accurate minwise hashing"):
  with members:
    - "ssh sketch shingle hash for indexing massive scale time series"
    - "the optimality of correlated sampling"
    - "hashing for statistics over k partitions"

Connected through hyperedge HE-7 (induced by source paper "range efficient consistent sampling and locality sensitive hashing for polygons"):
  with members:
    - "approximately minwise independence with twisted tabulation"
    - "bottom k and priority sampling set similarity and subset sums with minimal independence"

Connected through hyperedge HE-8 (induced by source paper "improved consistent weighted sampling revisited"):
  with members:
    - "bayesian locality sensitive hashing for fast similarity search"

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)