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: "toward a classification of finite partial monitoring games"
  Abstract: Partial-monitoring games constitute a mathematical framework for sequential decision making problems with imperfect feedback: The learner repeatedly chooses an action, opponent responds with an outcome, and then the learner suffers a loss and receives a feedback signal, both of which are fixed functions of the action and the outcome. The goal of the learner is to minimize his total cumulative loss. We make progress towards the classification of these games based on their minimax expected regret. Namely, we classify almost all games with two outcomes and finite number of actions: We show that their minimax expected regret is either zero, $\widetilde{\Theta}(\sqrt{T})$, $\Theta(T^{2/3})$, or $\Theta(T)$ and we give a simple and efficiently computable classification of these four classes of games. Our hope is that the result can serve as a stepping stone toward classifying all finite partial-monitoring games.

Connected through hyperedge HE-1 (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"
    - "towards optimal algorithms for prediction with expert advice"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "online learning with graph structured feedback against adaptive adversaries"):
  with members:
    - "online learning with feedback graphs beyond bandits"
    - "nonstochastic multi armed bandits with graph structured feedback"
    - "bandits with switching costs t 2 3 regret"
    - "online learning with switching costs and other adaptive adversaries"
    - "online bandit learning against an adaptive adversary from regret to policy regret"
    - "from bandits to experts on the value of side observations"

Connected through hyperedge HE-3 (induced by source paper "adaptive contract design for crowdsourcing markets bandit algorithms for repeated principal agent problems"):
  with members:
    - "contextual bandits with global constraints and objective"
    - "incentivizing high quality crowdwork"
    - "bandits with concave rewards and convex knapsacks"
    - "resourceful contextual bandits"
    - "bandit algorithms for tree search"
    - "bandits with knapsacks"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "dynamic pricing with limited supply"

Connected through hyperedge HE-4 (induced by source paper "online learning with composite loss functions"):
  with members:
    - "bandits with switching costs t 2 3 regret"
    - "online learning with switching costs and other adaptive adversaries"
    - "online bandit learning against an adaptive adversary from regret to policy regret"

Connected through hyperedge HE-5 (induced by source paper "regret analysis of stochastic and nonstochastic multi armed bandit problems"):
  with members:
    - "bandit algorithms for tree search"
    - "towards minimax policies for online linear optimization with bandit feedback"
    - "online bandit learning against an adaptive adversary from regret to policy regret"
    - "regret bounds for restless markov bandits"
    - "from bandits to experts on the value of side observations"
    - "efficient optimal learning for contextual bandits"
    - "no internal regret via neighborhood watch"
    - "on the universality of online mirror descent"

Connected through hyperedge HE-6 (induced by source paper "no internal regret via neighborhood watch"):
  with members:
    - "internal regret with partial monitoring calibration based optimal 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.GT(Computer Science and Game Theory)