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: "oracle efficient online learning and auction design"
  Abstract: We consider the design of computationally efficient online learning algorithms in an adversarial setting in which the learner has access to an offline optimization oracle. We present an algorithm called Generalized Follow-the-Perturbed-Leader and provide conditions under which it is oracle-efficient while achieving vanishing regret. Our results make significant progress on an open problem raised by Hazan and Koren, who showed that oracle-efficient algorithms do not exist in general and asked whether one can identify properties under which oracle-efficient online learning may be possible. #R##N#Our auction-design framework considers an auctioneer learning an optimal auction for a sequence of adversarially selected valuations with the goal of achieving revenue that is almost as good as the optimal auction in hindsight, among a class of auctions. We give oracle-efficient learning results for: (1) VCG auctions with bidder-specific reserves in single-parameter settings, (2) envy-free item pricing in multi-item auctions, and (3) s-level auctions of Morgenstern and Roughgarden for single-item settings. The last result leads to an approximation of the overall optimal Myerson auction when bidders' valuations are drawn according to a fast-mixing Markov process, extending prior work that only gave such guarantees for the i.i.d. setting. #R##N#Finally, we derive various extensions, including: (1) oracle-efficient algorithms for the contextual learning setting in which the learner has access to side information (such as bidder demographics), (2) learning with approximate oracles such as those based on Maximal-in-Range algorithms, and (3) no-regret bidding in simultaneous auctions, resolving an open problem of Daskalakis and Syrgkanis.

Connected through hyperedge HE-1 (induced by source paper "private learning implies online learning an efficient reduction"):
  with members:
    - "passing tests without memorizing two models for fooling discriminators"
    - "efficient private algorithms for learning halfspaces"
    - "private pac learning implies finite littlestone dimension"
    - "learning in non convex games with an optimization oracle"
    - "how to use heuristics for differential privacy"
    - "calibrating noise to variance in adaptive data analysis"
    - "differentially private release and learning of threshold functions"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"

Connected through hyperedge HE-2 (induced by source paper "online pricing with reserve price constraint for personal data markets"):
  with members:
    - "introduction to online convex optimization"
    - "contextual search via intrinsic volumes"
    - "contextual bandits with surrogate losses margin bounds and efficient algorithms"
    - "the design of arbitrage free data pricing schemes"
    - "improved regret bounds for oracle based adversarial contextual bandits"
    - "efficient algorithms for adversarial contextual learning"
    - "multidimensional binary search for contextual decision making"
    - "watch and learn optimizing from revealed preferences feedback"

Connected through hyperedge HE-3 (induced by source paper "online learning for min max discrete problems"):
  with members:
    - "introduction to online convex optimization"
    - "learning in non convex games with an optimization oracle"
    - "efficient online linear optimization with approximation algorithms"
    - "efficient algorithms for adversarial contextual learning"
    - "the computational power of optimization in online learning"
    - "learning in auctions regret is hard envy is easy"
    - "secretary problems with convex costs"

Connected through hyperedge HE-4 (induced by source paper "computing stackelberg equilibria of large general sum games"):
  with members:
    - "a polynomial time algorithm for spatio temporal security games"
    - "security game with non additive utilities and multiple attacker resources"
    - "the mysteries of security games equilibrium computation becomes combinatorial algorithm design"
    - "faster and simpler algorithm for optimal strategies of blotto game"
    - "catcher evader games"
    - "bilinear games polynomial time algorithms for rank based subclasses"

Connected through hyperedge HE-5 (induced by source paper "learning in non convex games with an optimization oracle"):
  with members:
    - "introduction to online convex optimization"
    - "on convergence and stability of gans"
    - "efficient regret minimization in non convex games"
    - "fast rates for empirical risk minimization of strict saddle problems"
    - "the computational power of optimization in online learning"
    - "prediction by random walk perturbation"
    - "on the generalization ability of online learning algorithms for pairwise loss functions"

Connected through hyperedge HE-6 (induced by source paper "online improper learning with an approximation oracle"):
  with members:
    - "introduction to online convex optimization"
    - "efficient online linear optimization with approximation 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.LG(Machine Learning)