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: "near optimal bounds for online caching with machine learned advice"
  Abstract: In the model of online caching with machine learned advice, introduced by Lykouris and Vassilvitskii, the goal is to solve the caching problem with an online algorithm that has access to next-arrival predictions: when each input element arrives, the algorithm is given a prediction of the next time when the element will reappear. The traditional model for online caching suffers from an $\Omega(\log k)$ competitive ratio lower bound (on a cache of size $k$). In contrast, the augmented model admits algorithms which beat this lower bound when the predictions have low error, and asymptotically match the lower bound when the predictions have high error, even if the algorithms are oblivious to the prediction error. In particular, Lykouris and Vassilvitskii showed that there is a prediction-augmented caching algorithm with a competitive ratio of $O(1+\min(\sqrt{\eta/OPT}, \log k))$ when the overall $\ell_1$ prediction error is bounded by $\eta$, and $OPT$ is the cost of the optimal offline algorithm. #R##N#The dependence on $k$ in the competitive ratio is optimal, but the dependence on $\eta/OPT$ may be far from optimal. In this work, we make progress towards closing this gap. Our contributions are twofold. First, we provide an improved algorithm with a competitive ratio of $O(1 + \min((\eta/OPT)/k, 1) \log k)$. Second, we provide a lower bound of $\Omega(\log \min((\eta/OPT)/(k \log k), k))$.

Connected through hyperedge HE-1 (induced by source paper "online learning with imperfect hints"):
  with members:
    - "adaptive scale invariant online algorithms for learning linear models"
    - "parameter free online convex optimization with sub exponential noise"
    - "matrix free preconditioning in online learning"
    - "semi online bipartite matching"
    - "black box reductions for parameter free online learning in banach spaces"
    - "competitive caching with machine learned advice"
    - "online learning sufficient statistics and the burkholder method"
    - "coin betting and parameter free online learning"

Connected through hyperedge HE-2 (induced by source paper "online metric algorithms with untrusted predictions"):
  with members:
    - "scheduling with predictions and the price of misprediction"
    - "competitive ratio versus regret minimization achieving the best of both worlds"
    - "online computation with untrusted advice"
    - "optimal analysis of an online algorithm for the bipartite matching problem on a line"
    - "competitive caching with machine learned advice"
    - "the case for learned index structures"
    - "revenue optimization with approximate bid predictions"
    - "competitive paging algorithms"

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)