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: "competitive analysis of the top k ranking problem"
  Abstract: Motivated by applications in recommender systems, web search, social choice and crowdsourcing, we consider the problem of identifying the set of top K items from noisy pairwise comparisons. In our setting, we are non-actively given r pairwise comparisons between each pair of n items, where each comparison has noise constrained by a very general noise model called the strong stochastic transitivity (SST) model. We analyze the competitive ratio of algorithms for the top-K problem. In particular, we present a linear time algorithm for the top-K problem which has a competitive ratio of O( √ n); i.e. to solve any instance of top-K, our algorithm needs at most O( √ n) times as many samples needed as the best possible algorithm for that instance (in contrast, all previous known algorithms for the top-K problem have competitive ratios of Ω(n) or worse). We further show that this is tight: any algorithm for the top-K problem has competitive ratio at least Ω( √ n). Stern School of Business, New York University, email: xchen3@stern.nyu.edu Department of Computer Science, Princeton University, email: sgopi@cs.princeton.edu Department of Computer Science, Princeton University, email: jiemingm@cs.princeton.edu Department of Computer Science, Princeton University, email: js44@cs.princeton.edu 1

Connected through hyperedge HE-1 (induced by source paper "best item learning in random utility models with subset choices"):
  with members:
    - "active ranking with subset wise preferences"
    - "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"
    - "multi dueling bandits with dependent arms"
    - "data driven rank breaking for efficient rank aggregation"
    - "relative upper confidence bound for the k armed dueling bandit problem"

Connected through hyperedge HE-2 (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"
    - "parallel algorithms for select and partition with noisy comparisons"
    - "adversarial top k ranking"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "active ranking using pairwise comparisons"
    - "sorting from noisy information"

Connected through hyperedge HE-3 (induced by source paper "from pac to instance optimal sample complexity in the plackett luce model"):
  with members:
    - "active ranking with subset wise preferences"
    - "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"
    - "multi dueling bandits with dependent arms"
    - "multi dueling bandits and their application to online ranker evaluation"
    - "data driven rank breaking for efficient rank aggregation"
    - "noisy sorting without resampling"

Connected through hyperedge HE-4 (induced by source paper "pac battling bandits in the plackett luce model"):
  with members:
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "budget constrained multi armed bandits with multiple plays"
    - "the simulator understanding adaptive sampling in the moderate confidence regime"
    - "data driven rank breaking for efficient rank aggregation"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "relative upper confidence bound for the k armed dueling bandit problem"

Connected through hyperedge HE-5 (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"
    - "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"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "active ranking using pairwise comparisons"

Connected through hyperedge HE-6 (induced by source paper "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"):
  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"
    - "adversarial top k ranking"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "active ranking using pairwise comparisons"
    - "noisy sorting without resampling"

Connected through hyperedge HE-7 (induced by source paper "active ranking from pairwise comparisons and when parametric assumptions don t help"):
  with members:
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "tuned models of peer assessment in moocs"
    - "active ranking using pairwise comparisons"

Connected through hyperedge HE-8 (induced by source paper "double or nothing multiplicative incentive mechanisms for crowdsourcing"):
  with members:
    - "reliable crowdsourcing under the generalized dawid skene model"
    - "a permutation based model for crowd labeling optimal estimation and robustness"
    - "regularized minimax conditional entropy for crowdsourcing"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "approval voting and incentives in crowdsourcing"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets"
    - "reliable crowdsourcing for multi class labeling using coding theory"

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)