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: "approachability of convex sets in games with partial monitoring"
  Abstract: We provide a necessary and sufficient condition under which a convex set is approachable in a game with partial monitoring, i.e.\ where players do not observe their opponents' moves but receive random signals. This condition is an extension of Blackwell's Criterion in the full monitoring framework, where players observe at least their payoffs. When our condition is fulfilled, we construct explicitly an approachability strategy, derived from a strategy satisfying some internal consistency property in an auxiliary game. We also provide an example of a convex set, that is neither (weakly)-approachable nor (weakly)-excludable, a situation that cannot occur in the full monitoring case. We finally apply our result to describe an $\epsilon$-optimal strategy of the uninformed player in a zero-sum repeated game with incomplete information on one side.

Connected through hyperedge HE-1 (induced by source paper "exploration by optimisation in partial monitoring"):
  with members:
    - "on first order bounds variance and gap dependent bounds for adversarial bandits"
    - "beating stochastic and adversarial semi bandits optimally and simultaneously"
    - "connections between mirror descent thompson sampling and the information ratio"
    - "introduction to online convex optimization"
    - "an information theoretic approach to minimax regret in partial monitoring"
    - "improved path length regret bounds for bandits"
    - "cleaning up the neighborhood a full classification for adversarial partial monitoring"
    - "more adaptive algorithms for adversarial bandits"

Connected through hyperedge HE-2 (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-3 (induced by source paper "cleaning up the neighborhood a full classification for adversarial partial monitoring"):
  with members:
    - "no internal regret via neighborhood watch"

Connected through hyperedge HE-4 (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"
    - "towards minimax online learning with unknown time horizon"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "internal regret with partial monitoring calibration based optimal algorithms"

Connected through hyperedge HE-5 (induced by source paper "approachability of convex sets in generalized quitting games"):
  with members:
    - "approachability regret and calibration implications and equivalences"
    - "internal regret with partial monitoring calibration based optimal algorithms"

Connected through hyperedge HE-6 (induced by source paper "approachability regret and calibration implications and equivalences"):
  with members:
    - "response based approachability and its application to generalized no regret algorithms"
    - "internal regret with partial monitoring calibration based optimal algorithms"
    - "defensive forecasting for linear protocols"

Connected through hyperedge HE-7 (induced by source paper "calibration and internal no regret with partial monitoring"):
  with members:
    - "non asymptotic calibration and resolution"

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)