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: "imperfect recall abstractions with bounds in games"
  Abstract: Imperfect-recall abstraction has emerged as the leading paradigm for practical large-scale equilibrium computation in imperfect-information games. However, imperfect-recall abstractions are poorly understood, and only weak algorithm-specific guarantees on solution quality are known. We develop the first general, algorithm-agnostic, solution quality guarantees for Nash equilibria and approximate self-trembling equilibria computed in imperfect-recall abstractions, when implemented in the original (perfect-recall) game. Our results are for a class of games that generalizes the only previously known class of imperfect-recall abstractions for which any such results have been obtained. Further, our analysis is tighter in two ways, each of which can lead to an exponential reduction in the solution quality error bound.   We then show that for extensive-form games that satisfy certain properties, the problem of computing a bound-minimizing abstraction for a single level of the game reduces to a clustering problem, where the increase in our bound is the distance function. This reduction leads to the first imperfect-recall abstraction algorithm with solution quality bounds. We proceed to show a divide in the class of abstraction problems. If payoffs are at the same scale at all information sets considered for abstraction, the input forms a metric space, and this immediately yields a $2$-approximation algorithm for abstraction. Conversely, if this condition is not satisfied, we show that the input does not form a metric space. Finally, we provide computational experiments to evaluate the practical usefulness of the abstraction techniques. They show that running counterfactual regret minimization on such abstractions leads to good strategies in the original games.

Connected through hyperedge HE-1 (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"
    - "fast convergence of regularized learning in games"

Connected through hyperedge HE-2 (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"
    - "no regret learning in extensive form games with imperfect recall"

Connected through hyperedge HE-3 (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"
    - "extensive form game imperfect recall abstractions with bounds"
    - "no regret learning in extensive form games with imperfect recall"
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-4 (induced by source paper "multi tenant cross slice resource orchestration a deep reinforcement learning approach"):
  with members:
    - "low complexity distributed radio access network slicing algorithms and experimental results"
    - "distributed resource allocation for network slicing over licensed and unlicensed bands"
    - "dynamic network slicing for scalable fog computing systems with energy harvesting"
    - "performance optimization in mobile edge computing via deep reinforcement learning"
    - "network slicing for 5g with sdn nfv concepts architectures and challenges"
    - "a survey on mobile edge computing the communication perspective"
    - "near optimal behavior via approximate state abstraction"
    - "dynamic computation offloading for mobile edge computing with energy harvesting devices"

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"
    - "no regret learning in extensive form games with imperfect recall"

Connected through hyperedge HE-6 (induced by source paper "constructing imperfect recall abstractions to solve large extensive form games"):
  with members:
    - "no regret learning in extensive form games with imperfect recall"

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"
    - "no regret learning in extensive form games with imperfect recall"
    - "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"
    - "no regret learning in extensive form games with imperfect recall"
    - "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)