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 "semantic rl with action grammars data efficient learning of hierarchical task abstractions"):
  Abstract: Hierarchical Reinforcement Learning algorithms have successfully been applied to temporal credit assignment problems with sparse reward signals. However, state-of-the-art algorithms require manual specification of sub-task structures, a sample inefficient exploration phase or lack semantic interpretability. Humans, on the other hand, efficiently detect hierarchical sub-structures induced by their surroundings. It has been argued that this inference process universally applies to language, logical reasoning as well as motor control. Therefore, we propose a cognitive-inspired Reinforcement Learning architecture which uses grammar induction to identify sub-goal policies. By treating an on-policy trajectory as a sentence sampled from the policy-conditioned language of the environment, we identify hierarchical constituents with the help of unsupervised grammatical inference. The resulting set of temporal abstractions is called action grammar (Pastra & Aloimonos, 2012) and unifies symbolic and connectionist approaches to Reinforcement Learning. It can be used to facilitate efficient imitation, transfer and online learning.

Includes members:
  Member "feudal networks for hierarchical reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning functionally decomposed hierarchies for continuous control tasks")
  Member "stochastic neural networks for hierarchical reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "meta learning curiosity algorithms")
  Member "the option critic architecture":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "towards deep symbolic reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "analyzing differentiable fuzzy logic operators")
  Member "lexis an optimization framework for discovering the hierarchical structure of sequential data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "evolution of hierarchical structure reuse in igem synthetic dna sequences")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "identifying hierarchical structure in sequences a linear time algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cocopie making mobile ai sweet as pie compression compilation co design goes a long way")

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)