human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "feeling the bern adaptive estimators for bernoulli probabilities of pairwise comparisons"):
  Abstract: We study methods for aggregating pairwise comparison data in order to estimate outcome probabilities for future comparisons among a collection of n items. Working within a flexible framework that imposes only a form of strong stochastic transitivity (SST), we introduce an adaptivity index defined by the indifference sets of the pairwise comparison probabilities. In addition to measuring the usual worst-case risk of an estimator, this adaptivity index also captures the extent to which the estimator adapts to instance-specific difficulty relative to an oracle estimator. We prove three main results that involve this adaptivity index and different algorithms. First, we propose a three-step estimator termed Count-Randomize-Least squares (CRL), and show that it has adaptivity index upper bounded as $\sqrt{n}$ up to logarithmic factors. We then show that that conditional on the hardness of planted clique, no computationally efficient estimator can achieve an adaptivity index smaller than $\sqrt{n}$. Second, we show that a regularized least squares estimator can achieve a poly-logarithmic adaptivity index, thereby demonstrating a $\sqrt{n}$-gap between optimal and computationally achievable adaptivity. Finally, we prove that the standard least squares estimator, which is known to be optimally adaptive in several closely related problems, fails to adapt in the context of estimating pairwise probabilities.

Includes members:
  Member "improved sum of squares lower bounds for hidden clique and hidden submatrix problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "broadcast congested clique planted cliques and pseudorandom generators")
  Member "estimation from pairwise comparisons sharp minimax bounds with topology dependence":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ranking a set of objects a graph based least square approach")
  Member "simple robust and optimal ranking from pairwise comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "diameter based interactive structure discovery")
  Member "fast provable algorithms for isotonic regression in all ell_ p norms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "iterative refinement for ell_p norm regression")
  Member "sum of squares lower bounds for planted clique":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "broadcast congested clique planted cliques and pseudorandom generators")
  Member "on the hardness of signaling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the complexity of contracts")
  Member "noisy sorting without resampling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "searching trees with permanently noisy advice walking and query algorithms")
  Member "a direct formulation for sparse pca using semidefinite programming":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "supervised quantile normalization for low rank matrix factorization")

Question: Please tell me which class the source paper 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)