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: "learning equilibria of games via payoff queries"
  Abstract: A recent body of experimental literature has studied empirical game-theoretical analysis, in which we have partial knowledge of a game, consisting of observations of a subset of the pure-strategy profiles and their associated payoffs to players. The aim is to find an exact or approximate Nash equilibrium of the game, based on these observations. It is usually assumed that the strategy profiles may be chosen in an on-line manner by the algorithm. We study a corresponding computational learning model, and the query complexity of learning equilibria for various classes of games. We give basic results for bimatrix and graphical games. Our focus is on symmetric network congestion games. For directed acyclic networks, we can learn the cost functions (and hence compute an equilibrium) while querying just a small fraction of pure-strategy profiles. For the special case of parallel links, we have the stronger result that an equilibrium can be identified while only learning a small fraction of the cost values.

Connected through hyperedge HE-1 (induced by source paper "multiagent evaluation under incomplete information"):
  with members:
    - "emergent coordination through competition"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "a generalised method for empirical game theoretic analysis"
    - "re evaluating evaluation"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "population based training of neural networks"
    - "giraffe using deep reinforcement learning to play chess"
    - "pagerank optimization by edge selection"

Connected through hyperedge HE-2 (induced by source paper "distributed methods for computing approximate equilibria"):
  with members:
    - "settling the complexity of computing approximate two player nash equilibria"
    - "communication complexity of approximate nash equilibria"
    - "an empirical study of finding approximate equilibria in bimatrix games"
    - "approximate well supported nash equilibria in symmetric bimatrix games"
    - "finding approximate nash equilibria of bimatrix games via payoff queries"
    - "on the communication complexity of approximate nash equilibria"
    - "approximate well supported nash equilibria below two thirds"
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-3 (induced by source paper "learning equilibria of simulation based games"):
  with members:
    - "a generalised method for empirical game theoretic analysis"
    - "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"
    - "aivat a new variance reduction technique for agent evaluation in imperfect information games"
    - "analyzing incentives for protocol compliance in complex domains a case study of introduction based routing"
    - "query complexity of approximate nash equilibria"

Connected through hyperedge HE-4 (induced by source paper "near optimal communication lower bounds for approximate nash equilibria"):
  with members:
    - "distributed methods for computing approximate equilibria"
    - "the communication complexity of local search"
    - "complexity theory game theory and economics"
    - "communication complexity of correlated equilibrium in two player games"
    - "query to communication lifting for bpp"
    - "the query complexity of correlated equilibria"
    - "settling the complexity of computing approximate two player nash equilibria"
    - "communication complexity of approximate nash equilibria"

Connected through hyperedge HE-5 (induced by source paper "learning convex partitions and computing game theoretic equilibria from best response queries"):
  with members:
    - "the query complexity of correlated equilibria"
    - "communication complexity of approximate nash equilibria"
    - "a counter example to karlin s strong conjecture for fictitious play"
    - "query complexity of approximate equilibria in anonymous games"
    - "empirical distribution of equilibrium play and its testing application"
    - "query complexity of approximate nash equilibria"
    - "on the approximation performance of fictitious play in finite games"
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-6 (induced by source paper "communication complexity of approximate nash equilibria"):
  with members:
    - "distributed methods for computing approximate equilibria"
    - "the query complexity of correlated equilibria"
    - "settling the complexity of computing approximate two player nash equilibria"
    - "well supported versus approximate nash equilibria query complexity of large games"
    - "inapproximability of nash equilibrium"
    - "computational complexity of approximate nash equilibrium in large games"
    - "on the communication complexity of approximate nash equilibria"
    - "query complexity of approximate nash equilibria"

Connected through hyperedge HE-7 (induced by source paper "the query complexity of correlated equilibria"):
  with members:
    - "well supported versus approximate nash equilibria query complexity of large games"
    - "empirical distribution of equilibrium play and its testing application"
    - "query complexity of approximate nash equilibria"
    - "query complexity of correlated equilibrium"
    - "polynomial time computation of exact correlated equilibrium in compact games"
    - "simple strategies for large zero sum games with applications to complexity theory"

Connected through hyperedge HE-8 (induced by source paper "generalized mirror descents in congestion games"):
  with members:
    - "convergence to equilibrium of logit dynamics for strategic games"
    - "concurrent imitation dynamics in congestion games"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

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)