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: "top k ranking from pairwise comparisons when spectral ranking is optimal"
  Abstract: We explore the top-$K$ rank aggregation problem. Suppose a collection of items is compared in pairs repeatedly, and we aim to recover a consistent ordering that focuses on the top-$K$ ranked items based on partially revealed preference information. We investigate the Bradley-Terry-Luce model in which one ranks items according to their perceived utilities modeled as noisy observations of their underlying true utilities. Our main contributions are two-fold. First, in a general comparison model where item pairs to compare are given a priori, we attain an upper and lower bound on the sample size for reliable recovery of the top-$K$ ranked items. Second, more importantly, extending the result to a random comparison model where item pairs to compare are chosen independently with some probability, we show that in slightly restricted regimes, the gap between the derived bounds reduces to a constant factor, hence reveals that a spectral method can achieve the minimax optimality on the (order-wise) sample size required for top-$K$ ranking. That is to say, we demonstrate a spectral method alone to be sufficient to achieve the optimality and advantageous in terms of computational complexity, as it does not require an additional stage of maximum likelihood estimation that a state-of-the-art scheme employs to achieve the optimality. We corroborate our main results by numerical experiments.

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"
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "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 "learning mixtures of plackett luce models from structured partial orders"):
  with members:
    - "a cost effective framework for preference elicitation and aggregation"
    - "learning mixtures of plackett luce models"
    - "data driven rank breaking for efficient rank aggregation"
    - "efficient probabilistic inference with partial ranking queries"
    - "active ranking using pairwise comparisons"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "matrix completion from noisy entries"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-3 (induced by source paper "secretary ranking with minimal inversions"):
  with members:
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "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-4 (induced by source paper "worst case vs average case design for estimation from fixed pairwise comparisons"):
  with members:
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "competitive analysis of the top k ranking problem"
    - "feeling the bern adaptive estimators for bernoulli probabilities of pairwise comparisons"
    - "a permutation based model for crowd labeling optimal estimation and robustness"
    - "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"
    - "active ranking using pairwise comparisons"

Connected through hyperedge HE-5 (induced by source paper "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"):
  with members:
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "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"
    - "noisy sorting without resampling"

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"
    - "efficient algorithms for adversarial contextual learning"
    - "sorting from noisy information"

Connected through hyperedge HE-7 (induced by source paper "competitive analysis of the top k ranking problem"):
  with members:
    - "adversarial top k ranking"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "simulating noisy channel interaction"
    - "multiple identifications in multi armed bandits"
    - "rank centrality ranking from pair wise comparisons"
    - "active ranking using pairwise comparisons"
    - "noisy sorting without resampling"

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)