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 bayesian games"
  Abstract: Recent price-of-anarchy analyses of games of complete information suggest that coarse correlated equilibria, which characterize outcomes resulting from no-regret learning dynamics, have near-optimal welfare. This work provides two main technical results that lift this conclusion to games of incomplete information, a.k.a., Bayesian games. First, near-optimal welfare in Bayesian games follows directly from the smoothness-based proof of near-optimal welfare in the same game when the private information is public. Second, no-regret learning dynamics converge to Bayesian coarse correlated equilibrium in these incomplete information games. These results are enabled by interpretation of a Bayesian game as a stochastic game of complete information.

Connected through hyperedge HE-1 (induced by source paper "discovering strategic behaviors for collaborative content production in social networks"):
  with members:
    - "inf vae a variational autoencoder framework to integrate homophily and influence in diffusion prediction"
    - "an induced multi relational framework for answer selection in community question answer platforms"
    - "dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems"
    - "session based social recommendation via dynamic graph attention networks"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "attention models in graphs a survey"
    - "deep learning on graphs a survey"

Connected through hyperedge HE-2 (induced by source paper "robust multi agent counterfactual prediction"):
  with members:
    - "theory of minds understanding behavior in groups through inverse planning"
    - "deep counterfactual regret minimization"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "m 3 rl mind aware multi agent management reinforcement learning"
    - "reinforcement learning and inverse reinforcement learning with system 1 and system 2"
    - "optimal auctions through deep learning"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "deep reinforcement learning from self play in imperfect information games"

Connected through hyperedge HE-3 (induced by source paper "on the tractability of public persuasion with no externalities"):
  with members:
    - "algorithmic persuasion with no externalities"
    - "bayesian exploration incentivizing exploration in bayesian games"
    - "algorithmic bayesian persuasion"
    - "deterministic algorithms for submodular maximization problems"
    - "hardness results for signaling in bayesian zero sum and network routing games"
    - "honest signaling in zero sum games is hard and lying is even harder"
    - "mixture selection mechanism design and signaling"
    - "tight bounds on ell_1 approximation and learning of self bounding functions"

Connected through hyperedge HE-4 (induced by source paper "learning to correlate in multi player general sum sequential games"):
  with members:
    - "computing optimal coarse correlated equilibria in sequential games"
    - "solving imperfect information games via discounted regret minimization"
    - "online convex optimization for sequential decision processes and extensive form games"
    - "revisiting cfr and alternating updates"
    - "deep counterfactual regret minimization"
    - "safe and nested subgame solving for imperfect information games"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "finding any nontrivial coarse correlated equilibrium is hard"

Connected through hyperedge HE-5 (induced by source paper "bayesian persuasion with sequential games"):
  with members:
    - "targeting and signaling in ad auctions"
    - "computational aspects of private bayesian persuasion"
    - "algorithmic bayesian persuasion"
    - "constrained signaling in auction design"
    - "sequential auctions and externalities"

Connected through hyperedge HE-6 (induced by source paper "computing optimal coarse correlated equilibria in sequential games"):
  with members:
    - "safe and nested subgame solving for imperfect information games"
    - "computational results for extensive form adversarial team games"
    - "polynomial time computation of exact correlated equilibrium in compact games"

Connected through hyperedge HE-7 (induced by source paper "contextual bandits with cross learning"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "incentive aware contextual pricing with non parametric market noise"
    - "learning to bid without knowing your value"
    - "learning multi item auctions with or without samples"
    - "selling to a no regret buyer"
    - "learning simple auctions"
    - "online learning in repeated auctions"
    - "online learning with feedback graphs beyond bandits"

Connected through hyperedge HE-8 (induced by source paper "an improved welfare guarantee for first price auctions"):
  with members:
    - "the price of anarchy in auctions"
    - "on the efficiency of all pay mechanisms"
    - "price of anarchy for auction revenue"
    - "tight bounds for the price of anarchy of simultaneous first price auctions"
    - "composable and efficient mechanisms"
    - "price of anarchy for greedy auctions"

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)