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: "robust collaborative online learning"
  Abstract: We consider a collaborative variant of prediction with expert advice. In each round, one user wishes to make a prediction, and must choose which expert to follow. The users would like to share their experiences in order to learn faster--ideally they could amortize the same total regret cross the whole community of users. However, some of the users may behave maliciously, distorting their reported payoffs in order to manipulate the honest users of the system. And even if all users behave honestly, different experts may perform better for different users, such that sharing data can be counterproductive. #R##N#We present a robust collaborative algorithm for prediction with expert advice, which guarantees that every subset of users H perform nearly as well as if they had shared all of their data, and ignored all data from users outside of H. This algorithm limits the damage done by the dishonest users to O(root(T)), compared to the O(T) we would obtain by naively aggregating data. #R##N#We also extend our results to general online convex optimization. The resulting algorithm achieves low regret, but is computationally intractable. This demonstrates that there is no statistical obstruction to generalizing robust collaborative online learning, but leaves the design of efficient algorithms as an open problem.

Connected through hyperedge HE-1 (induced by source paper "avoiding imposters and delinquents adversarial crowdsourcing and peer prediction"):
  with members:
    - "multisection in the stochastic block model using semidefinite programming"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "truth serums for massively crowdsourced evaluation tasks"
    - "how robust are reconstruction thresholds for community detection"
    - "approval voting and incentives in crowdsourcing"
    - "provably manipulation resistant reputation systems"
    - "double or nothing multiplicative incentive mechanisms for crowdsourcing"
    - "crowdsourced judgement elicitation with endogenous proficiency"

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)