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 "loss annealed gail for sample efficient and stable imitation learning"):
  Abstract: Imitation learning is the problem of learning a policy from an expert policy without access to a reward signal. Often, the expert policy is only available in the form of expert demonstrations. Behavior cloning and GAIL are two popularly used methods for performing imitation learning in this setting. Behavior cloning converges in a few training iterations, but doesn't reach peak performance and suffers from compounding errors due to its supervised training framework and iid assumption. GAIL attempts to tackle this problem by accounting for the temporal dependencies between states while matching occupancy measures of the expert and the policy. Although GAIL has shown successes in a number of environments, it takes a lot of environment interactions. Given their complementary benefits, existing methods have mentioned trying or tried to combine the two methods, without much success. We look at some of the limitations of existing ideas that try to combine BC and GAIL, and present an algorithm that combines the best of both worlds to enable faster and stable training while not compromising on performance. Our algorithm is embarrassingly simple to implement and seamlessly integrates with different policy gradient algorithms. We demonstrate the effectiveness of the algorithm both in low dimensional control tasks in a limited data setting, and in high dimensional grid world environments.

Includes members:
  Member "on the difficulty of warm starting neural network training":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep batch active learning by diverse uncertain gradient lower bounds")
  Member "rethinking imagenet pre training":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "addressing function approximation error in actor critic methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning")
  Member "learning goal embeddings via self play for hierarchical reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reinforcement learning for combinatorial optimization a survey")
  Member "learning robust rewards with adversarial inverse reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust deep reinforcement learning against adversarial perturbations on observations")
  Member "infogail interpretable imitation learning from visual demonstrations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")
  Member "proximal policy optimization algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "generative adversarial imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep reinforcement learning for autonomous driving a survey")

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)