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 "i m sorry dave i m afraid i can t do that deep q learning from forbidden action"):
  Abstract: The use of Reinforcement Learning (RL) is still restricted to simulation or to enhance human-operated systems through recommendations. Real-world environments (e.g. industrial robots or power grids) are generally designed with safety constraints in mind implemented in the shape of valid actions masks or contingency controllers. For example, the range of motion and the angles of the motors of a robot can be limited to physical boundaries. Violating constraints thus results in rejected actions or entering in a safe mode driven by an external controller, making RL agents incapable of learning from their mistakes. In this paper, we propose a simple modification of a state-of-the-art deep RL algorithm (DQN), enabling learning from forbidden actions. To do so, the standard Q-learning update is enhanced with an extra safety loss inspired by structured classification. We empirically show that it reduces the number of hit constraints during the learning phase and accelerates convergence to near-optimal policies compared to using standard DQN. Experiments are done on a Visual Grid World Environment and Text-World domain.

Includes members:
  Member "learning action transferable policy with action embedding":
    also connected through other hyperedges:
      (none)
  Member "learning action representations for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "leaky tiling activations a simple approach to learning sparse representations online")
  Member "ledeepchef deep reinforcement learning agent for families of 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 "the natural language of actions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "language is power representing states using natural language in reinforcement learning")
  Member "babyai first steps towards grounded language learning with a human in the loop":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the interaction between supervision and self play in emergent communication")
  Member "textworld a learning environment for text based games":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning dynamic knowledge graphs to generalize on text based games")
  Member "observe and look further achieving consistent performance on atari":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")
  Member "learn what not to learn action elimination with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning dynamic knowledge graphs to generalize on text based games")

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)