human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "correlated adversarial imitation learning"):
  Abstract: A novel imitation learning algorithm is introduced by applying a game-theoretic notion of correlated equilibrium to the generative adversarial imitation learning. This imitation learning algorithm is equipped with queues of discriminators and agents, in contrast with the classical approach, where there are single discriminator and single agent. #R##N#The achievement of a correlated equilibrium is due to a mediating neural architecture, which augments the observations that are being seen by queues of discriminators and agents. At every step of the training, the mediator network computes feedback using the rewards of discriminators and agents, to augment the next observations accordingly. By interacting in the game, it steers the training dynamic towards more suitable regions. #R##N#The resulting imitation learning provides three important benefits. First, it makes adaptability and transferability of the learned model to new environments straightforward. Second, it is suitable for imitating a mixture of state-action trajectories. #R##N#Third, it avoids the difficulties of non-convex optimization faced by the discriminator in the generative adversarial type architectures.

Includes members:
  Member "causal confusion in imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "maximum causal tsallis entropy imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "entropic regularization of markov decision processes")
  Member "quantifying generalization in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "learning in non convex games with an optimization oracle":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "convergence of adversarial training in overparametrized neural networks")
  Member "on convergence and stability of gans":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "blur noise and compression robust generative adversarial networks")
  Member "an online learning approach to generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "from poincar e recurrence to convergence in imperfect information games finding equilibrium via regularization")
  Member "efficient regret minimization in non convex games":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rnn based online learning an efficient first order optimization algorithm with a convergence guarantee")
  Member "infogail interpretable imitation learning from visual demonstrations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")

Question: Please tell me which class the source paper 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.LG(Machine Learning)