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: "towards optimal algorithms for prediction with expert advice"
  Abstract: We study the classical problem of prediction with expert advice in the adversarial setting with a geometric stopping time. In 1965, Cover gave the optimal algorithm for the case of 2 experts. In this paper, we design the optimal algorithm, adversary and regret for the case of 3 experts. Further, we show that the optimal algorithm for $2$ and $3$ experts is a probability matching algorithm (analogous to Thompson sampling) against a particular randomized adversary. Remarkably, our proof shows that the probability matching algorithm is not only optimal against this particular randomized adversary, but also minimax optimal. #R##N#Our analysis develops upper and lower bounds simultaneously, analogous to the primal-dual method. Our analysis of the optimal adversary goes through delicate asymptotics of the random walk of a particle between multiple walls. We use the connection we develop to random walks to derive an improved algorithm and regret bound for the case of $4$ experts, and, provide a general framework for designing the optimal algorithm and adversary for an arbitrary number of experts.

Connected through hyperedge HE-1 (induced by source paper "optimal anytime regret with two experts"):
  with members:
    - "krivine diffusions attain the goemans williamson approximation ratio"
    - "sticky brownian rounding and its applications to constraint satisfaction problems"
    - "tight lower bounds for multiplicative weights algorithmic families"
    - "geodesic walks in polytopes"
    - "towards minimax online learning with unknown time horizon"

Connected through hyperedge HE-2 (induced by source paper "adversarial policies in learning systems with malicious experts"):
  with members:
    - "tight lower bounds for multiplicative weights algorithmic families"

Connected through hyperedge HE-3 (induced by source paper "connections between mirror descent thompson sampling and the information ratio"):
  with members:
    - "first order regret analysis of thompson sampling"
    - "introduction to online convex optimization"
    - "an information theoretic approach to minimax regret in partial monitoring"
    - "sparsity variance and curvature in multi armed bandits"
    - "online learning with feedback graphs without the graphs"
    - "bandit convex optimization sqrt t regret in one dimension"
    - "online learning with feedback graphs beyond bandits"
    - "nonstochastic multi armed bandits with graph structured feedback"

Connected through hyperedge HE-4 (induced by source paper "an information theoretic approach to minimax regret in partial monitoring"):
  with members:
    - "more adaptive algorithms for adversarial bandits"
    - "sparsity variance and curvature in multi armed bandits"
    - "online learning with feedback graphs beyond bandits"
    - "learning to optimize via information directed sampling"
    - "an information theoretic analysis of thompson sampling"
    - "online linear optimization via smoothing"
    - "further optimal regret bounds for thompson sampling"
    - "towards minimax policies for online linear optimization with bandit feedback"

Connected through hyperedge HE-5 (induced by source paper "thompson sampling for adversarial bit prediction"):
  with members:
    - "first order regret analysis of thompson sampling"
    - "introduction to multi armed bandits"
    - "a tutorial on thompson sampling"
    - "an information theoretic analysis of thompson sampling"
    - "further optimal regret bounds for thompson sampling"
    - "the best of both worlds stochastic and adversarial bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "optimality of universal bayesian sequence prediction for general loss and alphabet"

Connected through hyperedge HE-6 (induced by source paper "new potential based bounds for the geometric stopping version of prediction with expert advice"):
  with members:
    - "new potential based bounds for prediction with expert advice"
    - "pde approach to the problem of online prediction with expert advice a construction of potential based strategies"
    - "tight lower bounds for multiplicative weights algorithmic families"

Connected through hyperedge HE-7 (induced by source paper "new potential based bounds for prediction with expert advice"):
  with members:
    - "new potential based bounds for the geometric stopping version of prediction with expert advice"
    - "pde approach to the problem of online prediction with expert advice a construction of potential based strategies"
    - "online prediction with selfish experts"

Connected through hyperedge HE-8 (induced by source paper "online learning with an almost perfect expert"):
  with members:
    - "online learning sufficient statistics and the burkholder method"
    - "online bandit learning against an adaptive adversary from regret to policy regret"
    - "a stochastic view of optimal regret through minimax duality"
    - "randomized rounding without solving the linear program"

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)