human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "an approximate dynamic programming approach to repeated games with vector losses"):
  Abstract: We describe an approximate dynamic programming (ADP) approach to compute approximately optimal strategies and approximations of the minimal losses that can be guaranteed in discounted repeated games with vector losses. At the core of our approach is a characterization of the lower Pareto frontier of the set of expected losses that a player can guarantee in these games as the unique fixed point of a set-valued dynamic programming (DP) operator. This fixed point can be approximated by an iterative application of this DP operator compounded by a polytopic set approximation, beginning with a single point. Each iteration can be computed by solving a set of linear programs corresponding to the vertices of the polytope. We derive rigorous bounds on the error of the resulting approximation and the performance of the corresponding approximately optimal strategies. #R##N#We discuss an application to regret minimization in repeated decision-making in adversarial environments, where we show that this approach can be used to compute approximately optimal strategies and approximations of the minimax optimal regret when the action sets are finite. We illustrate this approach by computing provably approximately optimal strategies for the problem of prediction using expert advice under discounted $\{0,1\}-$losses. Our numerical evaluations demonstrate the sub-optimality of well-known off-the-shelf online learning algorithms like Hedge and a significantly improved performance on using our approximately optimal strategies in these settings. Our work thus demonstrates the significant potential in using the ADP framework to design effective online learning algorithms.

Includes members:
  Member "towards optimal algorithms for prediction with expert advice":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimal anytime regret with two experts")
  Member "approachability regret and calibration implications and equivalences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "regret optimal strategies for playing repeated games with discounted losses")
  Member "towards minimax online learning with unknown time horizon":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal anytime regret with two experts")
  Member "regret analysis of stochastic and nonstochastic multi armed bandit problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits")
  Member "internal regret with partial monitoring calibration based optimal algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approachability of convex sets in generalized quitting games")
  Member "approachability of convex sets in games with partial monitoring":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "exploration by optimisation in partial monitoring")

Question: Please tell me which class the source paper 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.GT(Computer Science and Game Theory)