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: "spectral mle top k rank aggregation from pairwise comparisons"
  Abstract: This paper explores the preference-based top-$K$ rank aggregation problem. Suppose that a collection of items is repeatedly compared in pairs, and one wishes to recover a consistent ordering that emphasizes the top-$K$ ranked items, based on partially revealed preferences. We focus on the Bradley-Terry-Luce (BTL) model that postulates a set of latent preference scores underlying all items, where the odds of paired comparisons depend only on the relative scores of the items involved. #R##N#We characterize the minimax limits on identifiability of top-$K$ ranked items, in the presence of random and non-adaptive sampling. Our results highlight a separation measure that quantifies the gap of preference scores between the $K^{\text{th}}$ and $(K+1)^{\text{th}}$ ranked items. The minimum sample complexity required for reliable top-$K$ ranking scales inversely with the separation measure irrespective of other preference distribution metrics. To approach this minimax limit, we propose a nearly linear-time ranking scheme, called \emph{Spectral MLE}, that returns the indices of the top-$K$ items in accordance to a careful score estimate. In a nutshell, Spectral MLE starts with an initial score estimate with minimal squared loss (obtained via a spectral method), and then successively refines each component with the assistance of coordinate-wise MLEs. Encouragingly, Spectral MLE allows perfect top-$K$ item identification under minimal sample complexity. The practical applicability of Spectral MLE is further corroborated 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"
    - "top k ranking from pairwise comparisons when spectral ranking is optimal"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "least squares ranking on graphs"

Connected through hyperedge HE-2 (induced by source paper "combinatorial bandits with relative feedback"):
  with members:
    - "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"
    - "mnl bandit a dynamic learning approach to assortment selection"
    - "multi dueling bandits with dependent arms"
    - "thompson sampling for the mnl bandit"
    - "dcm bandits learning to rank with multiple clicks"
    - "multi dueling bandits and their application to online ranker evaluation"

Connected through hyperedge HE-3 (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"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "active ranking using pairwise comparisons"
    - "sorting from noisy information"

Connected through hyperedge HE-4 (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"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "concentration of the kirchhoff index for erdős renyi graphs"
    - "sorting from noisy information"
    - "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems"

Connected through hyperedge HE-5 (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"
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "simple robust and optimal ranking from pairwise comparisons"

Connected through hyperedge HE-6 (induced by source paper "quantifying consensus of rankings based on q support patterns"):
  with members:
    - "maximal rank correlation"
    - "optimizing positional scoring rules for rank aggregation"
    - "maximal rank correlation"
    - "a weighted correlation index for rankings with ties"
    - "a family of rank similarity measures based on maximized effectiveness difference"

Connected through hyperedge HE-7 (induced by source paper "rank aggregation via heterogeneous thurstone preference models"):
  with members:
    - "methods for ordinal peer grading"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "noisy sorting without resampling"

Connected through hyperedge HE-8 (induced by source paper "secretary ranking with minimal inversions"):
  with members:
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "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"
    - "pricing online decisions beyond auctions"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "on the k independence required by linear probing and minwise independence"

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)