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: "active ranking from pairwise comparisons and when parametric assumptions don t help"
  Abstract: We consider sequential or active ranking of a set of n items based on noisy pairwise comparisons. Items are ranked according to the probability that a given item beats a randomly chosen item, and ranking refers to partitioning the items into sets of pre-specified sizes according to their scores. This notion of ranking includes as special cases the identification of the top-k items and the total ordering of the items. We first analyze a sequential ranking algorithm that counts the number of comparisons won, and uses these counts to decide whether to stop, or to compare another pair of items, chosen based on confidence intervals specified by the data collected up to that point. We prove that this algorithm succeeds in recovering the ranking using a number of comparisons that is optimal up to logarithmic factors. This guarantee does not require any structural properties of the underlying pairwise probability matrix, unlike a significant body of past work on pairwise ranking based on parametric models such as the Thurstone or Bradley-Terry-Luce models. It has been a long-standing open question as to whether or not imposing these parametric assumptions allows for improved ranking algorithms. For stochastic comparison models, in which the pairwise probabilities are bounded away from zero, our second contribution is to resolve this issue by proving a lower bound for parametric models. This shows, perhaps surprisingly, that these popular parametric modeling choices offer at most logarithmic gains for stochastic comparisons.

Connected through hyperedge HE-1 (induced by source paper "ranking a set of objects a graph based least square approach"):
  with members:
    - "graph resistance and learning from pairwise comparisons"
    - "maximum selection and ranking under noisy comparisons"
    - "top k ranking from pairwise comparisons when spectral ranking is optimal"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "least squares ranking on graphs"

Connected through hyperedge HE-2 (induced by source paper "on sample complexity upper and lower bounds for exact ranking from noisy comparisons"):
  with members:
    - "from pac to instance optimal sample complexity in the plackett luce model"
    - "active ranking with subset wise preferences"
    - "approximate ranking from pairwise comparisons"
    - "adaptive sampling for coarse ranking"
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "maximum selection and ranking under noisy comparisons"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "simple robust and optimal ranking from pairwise comparisons"

Connected through hyperedge HE-3 (induced by source paper "zeroth order non convex optimization with dueling choice bandits"):
  with members:
    - "active classification with comparison queries"
    - "multi dueling bandits with dependent arms"
    - "hyperparameter optimization a spectral approach"
    - "on kernelized multi armed bandits"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-4 (induced by source paper "thresholding bandit problem with both duels and pulls"):
  with members:
    - "active classification with comparison queries"
    - "thresholding bandits with augmented ucb"
    - "adaptive multiple arm identification"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "statistical decision making for optimal budget allocation in crowd labeling"
    - "multiple identifications in multi armed bandits"

Connected through hyperedge HE-5 (induced by source paper "adaptive estimation for approximate k nearest neighbor computations"):
  with members:
    - "approximate ranking from pairwise comparisons"
    - "adaptive monte carlo optimization"
    - "implicit maximum likelihood estimation"
    - "hyperband a novel bandit based approach to hyperparameter optimization"

Connected through hyperedge HE-6 (induced by source paper "secretary ranking with minimal inversions"):
  with members:
    - "top k ranking from pairwise comparisons when spectral ranking is optimal"
    - "parallel algorithms for select and partition with noisy comparisons"
    - "beyond matroids secretary problem and prophet inequality with general constraints"
    - "secretary problems with non uniform arrival order"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "pricing online decisions beyond auctions"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "on the k independence required by linear probing and minwise independence"

Connected through hyperedge HE-7 (induced by source paper "comparison based learning from weak oracles"):
  with members:
    - "learning combinatorial functions from pairwise comparisons"
    - "leveraging transitive relations for crowdsourced joins"
    - "crowder crowdsourcing entity resolution"
    - "comparison based learning with rank nets"
    - "adaptively learning the crowd kernel"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "approximate nearest neighbor search for low dimensional queries"
    - "hierarchical routing over dynamic wireless networks"

Connected through hyperedge HE-8 (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"
    - "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"
    - "sorting from noisy information"

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.LG(Machine Learning)