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 minimax online learning with unknown time horizon"
  Abstract: We consider online learning when the time horizon is unknown. We apply a minimax analysis, beginning with the fixed horizon case, and then moving on to two unknown-horizon settings, one that assumes the horizon is chosen randomly according to some known distribution, and the other which allows the adversary full control over the horizon. For the random horizon setting with restricted losses, we derive a fully optimal minimax algorithm. And for the adversarial horizon setting, we prove a nontrivial lower bound which shows that the adversary obtains strictly more power than when the horizon is fixed and known. Based on the minimax solution of the random horizon setting, we then propose a new adaptive algorithm which "pretends" that the horizon is drawn from a distribution from a special family, but no matter how the actual horizon is chosen, the worst-case regret is of the optimal rate. Furthermore, our algorithm can be combined and applied in many ways, for instance, to online convex optimization, follow the perturbed leader, exponential weights algorithm and first order bounds. Experiments show that our algorithm outperforms many other existing algorithms in an online linear optimization setting.

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 optimal algorithms for prediction with expert advice"

Connected through hyperedge HE-2 (induced by source paper "an approximate dynamic programming approach to repeated games with vector losses"):
  with members:
    - "towards optimal algorithms for prediction with expert advice"
    - "approachability regret and calibration implications and equivalences"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "internal regret with partial monitoring calibration based optimal algorithms"
    - "approachability of convex sets in games with partial monitoring"

Connected through hyperedge HE-3 (induced by source paper "regret optimal strategies for playing repeated games with discounted losses"):
  with members:
    - "towards optimal algorithms for prediction with expert advice"
    - "approachability regret and calibration implications and equivalences"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "prediction with expert advice under discounted loss"

Connected through hyperedge HE-4 (induced by source paper "repeated games with vector losses a set valued dynamic programming approach"):
  with members:
    - "towards optimal algorithms for prediction with expert advice"
    - "approachability regret and calibration implications and equivalences"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-5 (induced by source paper "a drifting games analysis for online learning and applications to boosting"):
  with members:
    - "prediction with advice of unknown number of experts"
    - "simultaneous model selection and optimization through parameter free stochastic learning"
    - "unconstrained online linear learning in hilbert spaces minimax algorithms and normal approximations"
    - "regret in online combinatorial optimization"
    - "relax and localize from value to algorithms"
    - "a parameter free hedging algorithm"

Connected through hyperedge HE-6 (induced by source paper "towards optimal algorithms for prediction with expert advice"):
  with members:
    - "unconstrained online linear learning in hilbert spaces minimax algorithms and normal approximations"
    - "minimax optimal algorithms for unconstrained linear optimization"
    - "online bandit learning against an adaptive adversary from regret to policy regret"
    - "a stochastic view of optimal regret through minimax duality"

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)