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: "no regret learning in extensive form games with imperfect recall"
  Abstract: Counterfactual Regret Minimization (CFR) is an efficient no-regret learning algorithm for decision problems modeled as extensive games. CFR's regret bounds depend on the requirement of perfect recall: players always remember information that was revealed to them and the order in which it was revealed. In games without perfect recall, however, CFR's guarantees do not apply. In this paper, we present the first regret bound for CFR when applied to a general class of games with imperfect recall. In addition, we show that CFR applied to any abstraction belonging to our general class results in a regret bound not just for the abstract game, but for the full game as well. We verify our theory and show how imperfect recall can be used to trade a small increase in regret for a significant reduction in memory in three domains: die-roll poker, phantom tic-tac-toe, and Bluff.

Connected through hyperedge HE-1 (induced by source paper "combining no regret and q learning"):
  with members:
    - "vortices instead of equilibria in minmax optimization chaos and butterfly effects of online learning in zero sum games"
    - "finite regret and cycles with fixed step size via alternating gradient descent ascent"
    - "stable predictive optimistic counterfactual regret minimization"
    - "learning to collaborate in markov decision processes"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "regret circuits composability of regret minimizers"
    - "solving imperfect information games via discounted regret minimization"
    - "deep counterfactual regret minimization"

Connected through hyperedge HE-2 (induced by source paper "optimistic regret minimization for extensive form games via dilated distance generating functions"):
  with members:
    - "introduction to online convex optimization"
    - "stable predictive optimistic counterfactual regret minimization"
    - "first order methods with increasing iterate averaging for solving saddle point problems"
    - "solving imperfect information games via discounted regret minimization"
    - "solving large sequential games with the excessive gap technique"
    - "depth limited solving for imperfect information games"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "imperfect recall abstractions with bounds in games"

Connected through hyperedge HE-3 (induced by source paper "computing large market equilibria using abstractions"):
  with members:
    - "markets for public decision making"
    - "deep counterfactual regret minimization"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "pacing equilibrium in first price auction markets"
    - "billion scale similarity search with gpus"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "convex program duality fisher markets and nash social welfare"
    - "imperfect recall abstractions with bounds in games"

Connected through hyperedge HE-4 (induced by source paper "limited lookahead in imperfect information games"):
  with members:
    - "depth limited solving for imperfect information games"
    - "robust stackelberg equilibria in extensive form games and extension to limited lookahead"
    - "safe and nested subgame solving for imperfect information games"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "imperfect recall abstractions with bounds in games"
    - "extensive form game imperfect recall abstractions with bounds"
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-5 (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"
    - "theoretical and practical advances on smoothing for extensive form games"
    - "regret minimization in behaviorally constrained zero sum games"
    - "extensive form perfect equilibrium computation in two player games"
    - "imperfect recall abstractions with bounds in games"

Connected through hyperedge HE-6 (induced by source paper "constructing imperfect recall abstractions to solve large extensive form games"):
  with members:
    - "imperfect recall abstractions with bounds in games"

Connected through hyperedge HE-7 (induced by source paper "regret minimization in behaviorally constrained zero sum games"):
  with members:
    - "smoothing method for approximate extensive form perfect equilibrium"
    - "theoretical and practical advances on smoothing for extensive form games"
    - "extensive form perfect equilibrium computation in two player games"
    - "imperfect recall abstractions with bounds in games"
    - "bayes bluff opponent modelling in poker"
    - "blackwell approachability and minimax theory"

Connected through hyperedge HE-8 (induced by source paper "theoretical and practical advances on smoothing for extensive form games"):
  with members:
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "imperfect recall abstractions with bounds in games"
    - "polynomial time computation of exact correlated equilibrium in compact games"

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)