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: "the simulator understanding adaptive sampling in the moderate confidence regime"
  Abstract: We propose a novel technique for analyzing adaptive sampling called the {\em Simulator}. Our approach differs from the existing methods by considering not how much information could be gathered by any fixed sampling strategy, but how difficult it is to distinguish a good sampling strategy from a bad one given the limited amount of data collected up to any given time. This change of perspective allows us to match the strength of both Fano and change-of-measure techniques, without succumbing to the limitations of either method. For concreteness, we apply our techniques to a structured multi-arm bandit problem in the fixed-confidence pure exploration setting, where we show that the constraints on the means imply a substantial gap between the moderate-confidence sample complexity, and the asymptotic sample complexity as $\delta \to 0$ found in the literature. We also prove the first instance-based lower bounds for the top-k problem which incorporate the appropriate log-factors. Moreover, our lower bounds zero-in on the number of times each \emph{individual} arm needs to be pulled, uncovering new phenomena which are drowned out in the aggregate sample complexity. Our new analysis inspires a simple and near-optimal algorithm for the best-arm and top-k identification, the first {\em practical} algorithm of its kind for the latter problem which removes extraneous log factors, and outperforms the state-of-the-art in experiments.

Connected through hyperedge HE-1 (induced by source paper "sequential mode estimation with oracle queries"):
  with members:
    - "a theoretical analysis of first heuristics of crowdsourced entity resolution"
    - "best arm identification in linear bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-2 (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"
    - "competitive analysis of the top k ranking problem"
    - "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-3 (induced by source paper "approximate ranking from pairwise comparisons"):
  with members:
    - "nearly instance optimal sample complexity bounds for top k arm selection"
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "on the gap between strict saddles and true convexity an omega log d lower bound for eigenvector approximation"):
  with members:
    - "communication efficient algorithms for distributed stochastic principal component analysis"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "optimal lower bounds for universal relation samplers and finding duplicates"
    - "lifting randomized query complexity to randomized communication complexity"
    - "how to escape saddle points efficiently"
    - "low rank approximation with entrywise ell_1 norm error"
    - "a geometric analysis of phase retrieval"

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)