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: "sorting and selection with imprecise comparisons"
  Abstract: We consider a simple model of imprecise comparisons: there exists some $\delta>0$ such that when a subject is given two elements to compare, if the values of those elements (as perceived by the subject) differ by at least $\delta$, then the comparison will be made correctly; when the two elements have values that are within $\delta$, the outcome of the comparison is unpredictable. This model is inspired by both imprecision in human judgment of values and also by bounded but potentially adversarial errors in the outcomes of sporting tournaments. #R##N#Our model is closely related to a number of models commonly considered in the psychophysics literature where $\delta$ corresponds to the {\em just noticeable difference unit (JND)} or {\em difference threshold}. In experimental psychology, the method of paired comparisons was proposed as a means for ranking preferences amongst $n$ elements of a human subject. The method requires performing all $\binom{n}{2}$ comparisons, then sorting elements according to the number of wins. The large number of comparisons is performed to counter the potentially faulty decision-making of the human subject, who acts as an imprecise comparator. #R##N#We show that in our model the method of paired comparisons has optimal accuracy, minimizing the errors introduced by the imprecise comparisons. However, it is also wasteful, as it requires all $\binom{n}{2}$. We show that the same optimal guarantees can be achieved using $4 n^{3/2}$ comparisons, and we prove the optimality of our method. We then explore the general tradeoff between the guarantees on the error that can be made and number of comparisons for the problems of sorting, max-finding, and selection. Our results provide strong lower bounds and close-to-optimal solutions for each of these problems.

Connected through hyperedge HE-1 (induced by source paper "locally private hypothesis selection"):
  with members:
    - "private hypothesis selection"
    - "pan private uniformity testing"
    - "sorted top k in rounds"
    - "improved differentially private analysis of variance"
    - "locally private learning without interaction requires separation"
    - "exponential separations in local differential privacy through communication complexity"
    - "the role of interactivity in local differential privacy"
    - "private identity testing for high dimensional distributions"

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"
    - "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-3 (induced by source paper "longest increasing subsequence under persistent comparison errors"):
  with members:
    - "optimal sorting with persistent comparison errors"
    - "sorting with recurrent comparison errors"
    - "noisy sorting without resampling"

Connected through hyperedge HE-4 (induced by source paper "sorting processes with energy constrained comparisons"):
  with members:
    - "noisy sorting without resampling"

Connected through hyperedge HE-5 (induced by source paper "on the optimality of tape merge of two lists with similar size"):
  with members:
    - "searching monotone multi dimensional arrays"

Connected through hyperedge HE-6 (induced by source paper "two body assignment problem in the context of the israeli medical internship match"):
  with members:
    - "fair and square cake cutting in two dimensions"
    - "an approximate law of one price in random assignment games"
    - "the and or game equilibrium characterization working paper"
    - "non price equilibria in markets of discrete goods"

Connected through hyperedge HE-7 (induced by source paper "how to select the largest k elements from evolving data"):
  with members:
    - "algorithms for multi armed bandit problems"
    - "sublinear algorithms for local graph centrality estimation"
    - "approximating pagerank locally with sublinear query complexity"

Connected through hyperedge HE-8 (induced by source paper "an active learning algorithm for ranking from pairwise preferences with an almost optimal query complexity"):
  with members:
    - "active ranking using pairwise comparisons"
    - "noisy sorting without resampling"
    - "learning to order things"

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.DS(Data Structures and Algorithms)