human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "foundations for restraining bolts reinforcement learning with ltlf ldlf restraining specifications"
  Abstract: In this work we investigate on the concept of "restraining bolt", envisioned in Science Fiction. Specifically we introduce a novel problem in AI. We have two distinct sets of features extracted from the world, one by the agent and one by the authority imposing restraining specifications (the "restraining bolt"). The two sets are apparently unrelated since of interest to independent parties, however they both account for (aspects of) the same world. We consider the case in which the agent is a reinforcement learning agent on the first set of features, while the restraining bolt is specified logically using linear time logic on finite traces LTLf/LDLf over the second set of features. We show formally, and illustrate with examples, that, under general circumstances, the agent can learn while shaping its goals to suitably conform (as much as possible) to the restraining bolt specifications.

Connected through hyperedge HE-1 (induced by source paper "learning non markovian reward models in mdps"):
  with members:
    - "learning based mean payoff optimization in an unknown mdp under omega regular constraints"
    - "safe reinforcement learning via shielding"

Connected through hyperedge HE-2 (induced by source paper "control synthesis from linear temporal logic specifications using model free reinforcement learning"):
  with members:
    - "logically constrained reinforcement learning"
    - "omega regular objectives in model free reinforcement learning"
    - "a policy search method for temporal logic specified reinforcement learning tasks"
    - "probabilistic motion planning under temporal tasks and soft constraints"
    - "q learning for robust satisfaction of signal temporal logic specifications"
    - "reinforcement learning with temporal logic rewards"
    - "correct by synthesis reinforcement learning with temporal logic constraints"
    - "verification of markov decision processes using learning algorithms"

Connected through hyperedge HE-3 (induced by source paper "deepsynth program synthesis for automatic task segmentation in deep reinforcement learning"):
  with members:
    - "learning concise models from long execution traces"
    - "logically constrained reinforcement learning"
    - "exact finite state machine identification from scenarios and temporal properties"
    - "logically constrained neural fitted q iteration"
    - "omega regular objectives in model free reinforcement learning"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "modular multitask reinforcement learning with policy sketches"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "modular deep reinforcement learning with temporal logic specifications"):
  with members:
    - "logically constrained neural fitted q iteration"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "strategic attentive writer for learning macro actions"
    - "modular multitask reinforcement learning with policy sketches"
    - "quantitative approximation of the probability distribution of a markov process by formal abstractions"
    - "continuous control with deep reinforcement learning"
    - "a learning based approach to control synthesis of markov decision processes for linear temporal logic specifications"
    - "probably approximately correct mdp learning and control with temporal logic constraints"

Question: Please tell me which class the center node 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)