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: "maximum selection and ranking under noisy comparisons"
  Abstract: We consider $(\epsilon,\delta)$-PAC maximum-selection and ranking for general probabilistic models whose comparisons probabilities satisfy strong stochastic transitivity and stochastic triangle inequality. Modifying the popular knockout tournament, we propose a maximum-selection algorithm that uses $\mathcal{O}\left(\frac{n}{\epsilon^2}\log \frac{1}{\delta}\right)$ comparisons, a number tight up to a constant factor. We then derive a general framework that improves the performance of many ranking algorithms, and combine it with merge sort and binary search to obtain a ranking algorithm that uses $\mathcal{O}\left(\frac{n\log n (\log \log n)^3}{\epsilon^2}\right)$ comparisons for any $\delta\ge\frac1n$, a number optimal up to a $(\log \log n)^3$ factor.

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"
    - "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"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "least squares ranking on graphs"

Connected through hyperedge HE-2 (induced by source paper "uncertainty about uncertainty near optimal adaptive algorithms for estimating binary mixtures of unknown coins"):
  with members:
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "learning populations of parameters"
    - "feeling the bern adaptive estimators for bernoulli probabilities of pairwise comparisons"
    - "a data quality metric dqm how to estimate the number of undetected errors in data sets"
    - "a polynomial lower bound for testing monotonicity"
    - "approval voting and incentives in crowdsourcing"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "testing probability distributions underlying aggregated data"

Connected through hyperedge HE-3 (induced by source paper "graph resistance and learning from pairwise comparisons"):
  with members:
    - "lectures on randomized numerical linear algebra"
    - "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"
    - "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-4 (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"
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "simple robust and optimal ranking from pairwise comparisons"

Connected through hyperedge HE-5 (induced by source paper "adaptive sampling for coarse ranking"):
  with members:
    - "practical algorithms for best k identification in multi armed bandits"
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "ordinal regression using noisy pairwise comparisons for body mass index range estimation"):
  with members:
    - "signet convolutional siamese network for writer independent offline signature verification"
    - "face to bmi using computer vision to infer body mass index on social media"
    - "rankiqa learning from rankings for no reference image quality assessment"
    - "learning to compare image patches via convolutional neural networks"
    - "adam a method for stochastic optimization"

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)