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: "safe reinforcement learning via shielding"
  Abstract: Reinforcement learning algorithms discover policies that maximize reward, but do not necessarily guarantee safety during learning or execution phases. We introduce a new approach to learn optimal policies while enforcing properties expressed in temporal logic. To this end, given the temporal logic specification that is to be obeyed by the learning system, we propose to synthesize a reactive system called a shield. The shield is introduced in the traditional learning process in two alternative ways, depending on the location at which the shield is implemented. In the first one, the shield acts each time the learning agent is about to make a decision and provides a list of safe actions. In the second way, the shield is introduced after the learning agent. The shield monitors the actions from the learner and corrects them only if the chosen action causes a violation of the specification. We discuss which requirements a shield must meet to preserve the convergence guarantees of the learner. Finally, we demonstrate the versatility of our approach on several challenging reinforcement learning scenarios.

Connected through hyperedge HE-1 (induced by source paper "learning to walk in the real world with minimal human effort"):
  with members:
    - "safety guided deep reinforcement learning via online gaussian process estimation"
    - "solving rubik s cube with a robot hand"
    - "generalizing from a few environments in safety critical reinforcement learning"
    - "data efficient reinforcement learning for legged robots"
    - "deepgait planning and control of quadrupedal gaits using deep reinforcement learning"
    - "norml no reward meta learning"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "learning agile and dynamic motor skills for legged robots"

Connected through hyperedge HE-2 (induced by source paper "cautious reinforcement learning with logical constraints"):
  with members:
    - "certified reinforcement learning with logic guidance"
    - "rethinking the discount factor in reinforcement learning a decision theoretic approach"
    - "reinforcement learning for temporal logic control synthesis with probabilistic satisfaction guarantees"
    - "modular deep reinforcement learning with temporal logic specifications"
    - "verifiably safe off model reinforcement learning"
    - "logically constrained reinforcement learning"
    - "logically constrained neural fitted q iteration"
    - "shielded decision making in mdps"

Connected through hyperedge HE-3 (induced by source paper "extended markov games to learn multiple tasks in multi agent reinforcement learning"):
  with members:
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "reinforcement learning for ltlf ldlf goals"
    - "hierarchical deep multiagent reinforcement learning"
    - "guided deep reinforcement learning for swarm systems"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "environment independent task specifications via gltl"
    - "safety aware apprenticeship learning"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-4 (induced by source paper "safe reinforcement learning for autonomous vehicles through parallel constrained policy optimization"):
  with members:
    - "addressing value estimation errors in reinforcement learning with a state action return distribution function"
    - "deep reinforcement learning for autonomous driving"
    - "virtual to real reinforcement learning for autonomous driving"
    - "constrained policy optimization"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "safe exploration of state and action spaces in reinforcement learning"
    - "policy gradients with variance related risk criteria"

Connected through hyperedge HE-5 (induced by source paper "safe reinforcement learning for probabilistic reachability and safety specifications a lyapunov based approach"):
  with members:
    - "value constrained model free continuous control"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "benchmarking deep reinforcement learning for continuous control"
    - "continuous control with deep reinforcement learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (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"
    - "foundations for restraining bolts reinforcement learning with ltlf ldlf restraining specifications"

Connected through hyperedge HE-7 (induced by source paper "imitation projected programmatic reinforcement learning"):
  with members:
    - "batch policy learning under constraints"
    - "control regularization for reduced variance reinforcement learning"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "houdini lifelong learning as program synthesis"
    - "safe exploration in continuous action spaces"
    - "predictor corrector policy optimization"
    - "programmatically interpretable reinforcement learning"
    - "accelerating imitation learning with predictive models"

Connected through hyperedge HE-8 (induced by source paper "certified reinforcement learning with logic guidance"):
  with members:
    - "the probabilistic model checker storm"
    - "deepsynth program synthesis for automatic task segmentation in deep reinforcement learning"
    - "rethinking the discount factor in reinforcement learning a decision theoretic approach"
    - "verifiably safe off model reinforcement learning"
    - "logically constrained reinforcement learning"
    - "playing hard exploration games by watching youtube"
    - "from game theoretic multi agent log linear learning to reinforcement learning"
    - "observe and look further achieving consistent performance on atari"

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.LO(Logic in Computer Science)