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: "sorting from noisy information"
  Abstract: This paper studies problems of inferring order given noisy information. In these problems there is an unknown order (permutation) $\pi$ on $n$ elements denoted by $1,...,n$. We assume that information is generated in a way correlated with $\pi$. The goal is to find a maximum likelihood $\pi^*$ given the information observed. We will consider two different types of observations: noisy comparisons and noisy orders. The data in Noisy orders are permutations given from an exponential distribution correlated with \pi (this is also called the Mallow's model). The data in Noisy Comparisons is a signal given for each pair of elements which is correlated with their true ordering. #R##N#In this paper we present polynomial time algorithms for solving both problems with high probability. As part of our proof we show that for both models the maximum likelihood solution $\pi^{\ast}$ is close to the original permutation $\pi$. #R##N#Our results are of interest in applications to ranking, such as ranking in sports, or ranking of search items based on comparisons by experts.

Connected through hyperedge HE-1 (induced by source paper "noise tolerant reliable active classification with comparison queries"):
  with members:
    - "the power of comparisons for actively learning linear classifiers"
    - "sorting by swaps with noisy comparisons"
    - "generalized comparison trees for point location problems"
    - "optimal sorting with persistent comparison errors"
    - "active classification with comparison queries"
    - "efficient learning of linear separators under bounded noise"
    - "minimax analysis of active learning"
    - "active and passive learning of linear separators under log concave distributions"

Connected through hyperedge HE-2 (induced by source paper "sorted top k in rounds"):
  with members:
    - "instance optimality in the noisy value and comparison model accept accept strong accept which papers get in"
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "competitive analysis of the top k ranking problem"
    - "parallel algorithms for select and partition with noisy comparisons"
    - "adversarial top k ranking"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "active ranking using pairwise comparisons"

Connected through hyperedge HE-3 (induced by source paper "graph resistance and learning from pairwise comparisons"):
  with members:
    - "lectures on randomized numerical linear algebra"
    - "maximum selection and ranking under noisy comparisons"
    - "worst case vs average case design for estimation from fixed pairwise comparisons"
    - "sorting and selection with imprecise comparisons"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "concentration of the kirchhoff index for erdős renyi graphs"
    - "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems"

Connected through hyperedge HE-4 (induced by source paper "optimal learning of mallows block model"):
  with members:
    - "efficiently learning mixtures of mallows models"
    - "learning mixtures of ranking models"
    - "consensus ranking under the exponential model"
    - "content modeling using latent permutations"
    - "statistical exponential families a digest with flash cards"

Connected through hyperedge HE-5 (induced by source paper "adaptive sampling for coarse ranking"):
  with members:
    - "maximum selection and ranking under noisy comparisons"
    - "practical algorithms for best k identification in multi armed bandits"
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "deep learning the city quantifying urban perception at a global scale"
    - "rank centrality ranking from pair wise comparisons"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "active ranking using pairwise comparisons"
    - "an active learning algorithm for ranking from pairwise preferences with an almost optimal query complexity"

Connected through hyperedge HE-6 (induced by source paper "maximum selection and ranking under noisy comparisons"):
  with members:
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "maximum selection and sorting with adversarial comparators and an application to density estimation"
    - "top k ranking from pairwise comparisons when spectral ranking is optimal"
    - "efficient algorithms for adversarial contextual learning"

Connected through hyperedge HE-7 (induced by source paper "clustering via crowdsourcing"):
  with members:
    - "parallel algorithms for select and partition with noisy comparisons"
    - "fault tolerant entity resolution with the crowd"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "on reverse pinsker inequalities"
    - "parallel correlation clustering on big graphs"
    - "exact recovery in the stochastic block model"
    - "crowder crowdsourcing entity resolution"
    - "sorting searching and simulation in the mapreduce framework"

Connected through hyperedge HE-8 (induced by source paper "data driven rank breaking for efficient rank aggregation"):
  with members:
    - "collaboratively learning preferences from ordinal data"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "learning mixed membership mallows models from pairwise comparisons"
    - "efficient estimation of word representations in vector space"
    - "random utility theory for social choice"
    - "rank centrality ranking from pair wise comparisons"

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)