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 "action assembly sparse imitation learning for text based games with combinatorial action spaces"):
  Abstract: We propose a computationally efficient algorithm that combines compressed sensing with imitation learning to solve sequential decision making text-based games with combinatorial action spaces. We propose a variation of the compressed sensing algorithm Orthogonal Matching Pursuit (OMP), that we call IK-OMP, and show that it can recover a bag-of-words from a sum of the individual word embeddings, even in the presence of noise. We incorporate IK-OMP into a supervised imitation learning setting and show that this algorithm, called Sparse Imitation Learning (Sparse-IL), solves the entire text-based game of Zork1 with an action space of approximately 10 million actions using imperfect, noisy demonstrations.

Includes members:
  Member "using reinforcement learning to learn how to play text based games":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exploration based language learning for text based games")
  Member "textworld a learning environment for text based games":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning dynamic knowledge graphs to generalize on text based games")
  Member "towards solving text based games by producing adaptive action spaces":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exploration based language learning for text based games")
  Member "counting to explore and generalize in text based games":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning dynamic knowledge graphs to generalize on text based games")
  Member "an actor critic algorithm for sequence prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "deep reinforcement learning in large discrete action spaces":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "i love your chain mail making knights smile in a fantasy game world open domain goal orientated dialogue agents")
  Member "deep reinforcement learning with a natural language action space":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "exploration based language learning for text based games")
  Member "language understanding for text based games using deep reinforcement 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)