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: "online computation with untrusted advice"
  Abstract: The advice model of online computation captures the setting in which the online algorithm is given some partial information concerning the request sequence. This paradigm allows to establish tradeoffs between the amount of this additional information and the performance of the online algorithm. However, unlike real life in which advice is a recommendation that we can chose to follow or to ignore based on trustworthiness, in the current advice model, the online algorithm treats it as infallible. This means that if the advice is corrupt or, worse, if it comes from a malicious source, the algorithm may perform poorly. In this work, we study online computation in a setting in which the advice is provided by an untrusted source. Our objective is to quantify the impact of untrusted advice so as to design and analyze online algorithms that are resilient and perform well even when the advice is generated in a malicious, adversarial manner. To this end, we focus on well-studied online problems such as ski rental, online bidding, bin packing, and list update. For ski-rental and online bidding, we show how to obtain algorithms that are Pareto-optimal with respect to the competitive ratios achieved; this improves upon the framework of Purohit et al. [NeurIPS 2018] in which Pareto-optimality is not necessarily guaranteed. For bin packing and list update, we give online algorithms with worst-case tradeoffs in their competitiveness, depending on whether the advice is trusted or not; this is motivated by work of Lykouris and Vassilvitskii [ICML 2018] on the paging problem, but in which the competitiveness depends on the reliability of the advice.

Connected through hyperedge HE-1 (induced by source paper "online metric algorithms with untrusted predictions"):
  with members:
    - "scheduling with predictions and the price of misprediction"
    - "near optimal bounds for online caching with machine learned advice"
    - "competitive ratio versus regret minimization achieving the best of both worlds"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "online algorithms for multi shop ski rental with machine learned predictions"):
  with members:
    - "optimal algorithms for ski rental with soft machine learned predictions"
    - "learning assisted competitive algorithms for peak aware energy scheduling"
    - "a model for learned bloom filters and optimizing by sandwiching"
    - "competitive caching with machine learned advice"
    - "revenue optimization with approximate bid predictions"
    - "the multi shop ski rental problem"
    - "rent lease or buy randomized algorithms for multislope ski rental"

Connected through hyperedge HE-3 (induced by source paper "competitive caching with machine learned advice"):
  with members:
    - "a model for learned bloom filters and optimizing by sandwiching"
    - "stochastic bandits robust to adversarial corruptions"
    - "revenue optimization with approximate bid predictions"
    - "online allocation with traffic spikes mixing adversarial and stochastic models"
    - "learning simple auctions"
    - "the sample complexity of revenue maximization"
    - "the limitations of optimization from samples"
    - "intriguing properties of neural networks"

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)