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: "theoretical and practical advances on smoothing for extensive form games"
  Abstract: Sparse iterative methods, in particular first-order methods, are known to be among the most effective in solving large-scale two-player zero-sum extensive-form games. The convergence rates of these methods depend heavily on the properties of the distance-generating function that they are based on. We investigate the acceleration of first-order methods for solving extensive-form games through better design of the dilated entropy function---a class of distance-generating functions related to the domains associated with the extensive-form games. By introducing a new weighting scheme for the dilated entropy function, we develop the first distance-generating function for the strategy spaces of sequential games that has no dependence on the branching factor of the player. This result improves the convergence rate of several first-order methods by a factor of $\Omega(b^dd)$, where $b$ is the branching factor of the player, and $d$ is the depth of the game tree. #R##N#Thus far, counterfactual regret minimization methods have been faster in practice, and more popular, than first-order methods despite their theoretically inferior convergence rates. Using our new weighting scheme and practical tuning we show that, for the first time, the excessive gap technique can be made faster than the fastest counterfactual regret minimization algorithm, CFR+, in practice.

Connected through hyperedge HE-1 (induced by source paper "large scale learning of agent rationality in two player zero sum games"):
  with members:
    - "what game are we playing end to end learning in normal and extensive form games"
    - "optnet differentiable optimization as a layer in neural networks"
    - "on differentiating parameterized argmin and argmax problems with application to bi level optimization"
    - "computational rationalization the inverse equilibrium problem"

Connected through hyperedge HE-2 (induced by source paper "online convex optimization for sequential decision processes and extensive form games"):
  with members:
    - "introduction to online convex optimization"
    - "what game are we playing end to end learning in normal and extensive form games"
    - "smoothing method for approximate extensive form perfect equilibrium"
    - "regret minimization for partially observable deep reinforcement learning"
    - "regret minimization in behaviorally constrained zero sum games"
    - "extensive form perfect equilibrium computation in two player games"
    - "imperfect recall abstractions with bounds in games"
    - "no regret learning in extensive form games with imperfect recall"

Connected through hyperedge HE-3 (induced by source paper "solving large extensive form games with strategy constraints"):
  with members:
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "a unified view of large scale zero sum equilibrium computation"
    - "measuring the size of large no limit poker games"
    - "bayes bluff opponent modelling in poker"

Connected through hyperedge HE-4 (induced by source paper "what game are we playing end to end learning in normal and extensive form games"):
  with members:
    - "optnet differentiable optimization as a layer in neural networks"
    - "on differentiating parameterized argmin and argmax problems with application to bi level optimization"
    - "adam a method for stochastic optimization"
    - "computational rationalization the inverse equilibrium problem"

Connected through hyperedge HE-5 (induced by source paper "solving large sequential games with the excessive gap technique"):
  with members:
    - "smoothing method for approximate extensive form perfect equilibrium"
    - "measuring the size of large no limit poker games"

Connected through hyperedge HE-6 (induced by source paper "regret minimization in behaviorally constrained zero sum games"):
  with members:
    - "smoothing method for approximate extensive form perfect equilibrium"
    - "extensive form perfect equilibrium computation in two player games"
    - "imperfect recall abstractions with bounds in games"
    - "no regret learning in extensive form games with imperfect recall"
    - "bayes bluff opponent modelling in poker"
    - "blackwell approachability and minimax theory"

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)