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: "noisy sorting without resampling"
  Abstract: In this paper we study noisy sorting without re-sampling. In this problem there is an unknown order $a_{\pi(1)}   \pi(j)$ for all pairs $i \neq j$, where $\ga > 0$ is a constant and $q(a_i,a_j) = -q(a_j,a_i)$ for all $i$ and $j$. It is assumed that the errors are independent. Given the status of the queries the goal is to find the maximum likelihood order. In other words, the goal is find a permutation $\sigma$ that minimizes the number of pairs $\sigma(i) > \sigma(j)$ where $q(\sigma(i),\sigma(j)) = -$. The problem so defined is the feedback arc set problem on distributions of inputs, each of which is a tournament obtained as a noisy perturbations of a linear order. Note that when $\ga < 1/2$ and $n$ is large, it is impossible to recover the original order $\pi$. #R##N#It is known that the weighted feedback are set problem on tournaments is NP-hard in general. Here we present an algorithm of running time $n^{O(\gamma^{-4})}$ and sampling complexity $O_{\gamma}(n \log n)$ that with high probability solves the noisy sorting without re-sampling problem. We also show that if $a_{\sigma(1)},a_{\sigma(2)},...,a_{\sigma(n)}$ is an optimal solution of the problem then it is ``close'' to the original order. More formally, with high probability it holds that $\sum_i |\sigma(i) - \pi(i)| = \Theta(n)$ and $\max_i |\sigma(i) - \pi(i)| = \Theta(\log n)$. #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 "searching trees with permanently noisy advice walking and query algorithms"):
  with members:
    - "binary search in graphs revisited"
    - "a general framework for robust interactive learning"
    - "submodular optimization under noise"
    - "deterministic and probabilistic binary search in graphs"

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 "from pac to instance optimal sample complexity in the plackett luce model"):
  with members:
    - "active ranking with subset wise preferences"
    - "pac ranking from pairwise and listwise queries lower bounds and upper bounds"
    - "pac battling bandits in the plackett luce model"
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "multi dueling bandits with dependent arms"
    - "competitive analysis of the top k ranking problem"
    - "multi dueling bandits and their application to online ranker evaluation"
    - "data driven rank breaking for efficient rank aggregation"

Connected through hyperedge HE-4 (induced by source paper "minimizing time to rank a learning and recommendation approach"):
  with members:
    - "composite marginal likelihood methods for random utility models"
    - "learning mixtures of plackett luce models"
    - "learning mixtures of ranking models"
    - "preference elicitation for general random utility models"
    - "vote elicitation complexity and strategy proofness"

Connected through hyperedge HE-5 (induced by source paper "rank aggregation via heterogeneous thurstone preference models"):
  with members:
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "methods for ordinal peer grading"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"

Connected through hyperedge HE-6 (induced by source paper "active ranking with subset wise preferences"):
  with members:
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "assortment optimisation under a general discrete choice model a tight analysis of revenue ordered assortments"
    - "data driven rank breaking for efficient rank aggregation"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "contextual dueling bandits"
    - "random utility theory for social choice"
    - "active ranking using pairwise comparisons"
    - "rank aggregation via nuclear norm minimization"

Connected through hyperedge HE-7 (induced by source paper "efficiently learning mixtures of mallows models"):
  with members:
    - "learning mixtures of plackett luce models"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "learning mixtures of ranking models"
    - "dirichlet process mixtures of generalized mallows models"
    - "tensor decompositions for learning latent variable models"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "polynomial learning of distribution families"

Connected through hyperedge HE-8 (induced by source paper "how many pairwise preferences do we need to rank a graph consistently"):
  with members:
    - "inductive pairwise ranking going beyond the n log n barrier"
    - "birank towards ranking on bipartite graphs"
    - "active ranking using pairwise comparisons"
    - "rank aggregation via nuclear norm minimization"
    - "an active learning algorithm for ranking from pairwise preferences with an almost optimal query complexity"

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)