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: "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
  Abstract: Data in the form of pairwise comparisons arises in many domains, including preference elicitation, sporting competitions, and peer grading among others. We consider parametric ordinal models for such pairwise comparison data involving a latent vector $w^* \in \mathbb{R}^d$ that represents the "qualities" of the $d$ items being compared; this class of models includes the two most widely used parametric models--the Bradley-Terry-Luce (BTL) and the Thurstone models. Working within a standard minimax framework, we provide tight upper and lower bounds on the optimal error in estimating the quality score vector $w^*$ under this class of models. The bounds depend on the topology of the comparison graph induced by the subset of pairs being compared via its Laplacian spectrum. Thus, in settings where the subset of pairs may be chosen, our results provide principled guidelines for making this choice. Finally, we compare these error rates to those under cardinal measurement models and show that the error rates in the ordinal and cardinal settings have identical scalings apart from constant pre-factors.

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"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "least squares ranking on graphs"

Connected through hyperedge HE-2 (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"
    - "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-3 (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"
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "statistical decision making for optimal budget allocation in crowd labeling"
    - "multiple identifications in multi armed bandits"

Connected through hyperedge HE-4 (induced by source paper "discovering context effects from raw choice data"):
  with members:
    - "comparison based choices"
    - "adam a method for stochastic optimization"
    - "learning to order things"

Connected through hyperedge HE-5 (induced by source paper "on strategyproof conference peer review"):
  with members:
    - "does my rebuttal matter insights from a major nlp conference"
    - "choosing how to choose papers"
    - "design and analysis of the nips 2016 review process"
    - "optimizing positional scoring rules for rank aggregation"
    - "strategyproof peer selection using randomization partitioning and apportionment"
    - "a near optimal mechanism for impartial selection"
    - "incentive design in peer review rating and repeated endogenous matching"
    - "tuned models of peer assessment in moocs"

Connected through hyperedge HE-6 (induced by source paper "approximate ranking from pairwise comparisons"):
  with members:
    - "nearly instance optimal sample complexity bounds for top k arm selection"
    - "the simulator understanding adaptive sampling in the moderate confidence regime"
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "tuned models of peer assessment in moocs"

Connected through hyperedge HE-7 (induced by source paper "hybrid mst a hybrid active sampling strategy for pairwise preference aggregation"):
  with members:
    - "random utility theory for social choice"
    - "active ranking using pairwise comparisons"

Connected through hyperedge HE-8 (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"
    - "top k ranking from pairwise comparisons when spectral ranking is optimal"
    - "a permutation based model for crowd labeling optimal estimation and robustness"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "active ranking using pairwise 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.LG(Machine Learning)