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: "a policy search method for temporal logic specified reinforcement learning tasks"
  Abstract: Reward engineering is an important aspect of reinforcement learning. Whether or not the user's intentions can be correctly encapsulated in the reward function can significantly impact the learning outcome. Current methods rely on manually crafted reward functions that often require parameter tuning to obtain the desired behavior. This operation can be expensive when exploration requires systems to interact with the physical world. In this paper, we explore the use of temporal logic (TL) to specify tasks in reinforcement learning. TL formula can be translated to a real-valued function that measures its level of satisfaction against a trajectory. We take advantage of this function and propose temporal logic policy search (TLPS), a model-free learning technique that finds a policy that satisfies the TL specification. A set of simulated experiments are conducted to evaluate the proposed approach.

Connected through hyperedge HE-1 (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"
    - "foundations for restraining bolts reinforcement learning with ltlf ldlf restraining specifications"
    - "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-2 (induced by source paper "prescribed performance control guided policy improvement for satisfying signal temporal logic tasks"):
  with members:
    - "q learning for robust satisfaction of signal temporal logic specifications"
    - "robust control for signal temporal logic specifications using average space robustness"
    - "path integral guided policy search"
    - "robust temporal logic model predictive control"
    - "a learning based approach to control synthesis of markov decision processes for linear temporal logic specifications"
    - "path integral policy improvement with covariance matrix adaptation"

Connected through hyperedge HE-3 (induced by source paper "average based robustness for continuous time signal temporal logic"):
  with members:
    - "arithmetic geometric mean robustness for control from signal temporal logic specifications"
    - "an milp approach for real time optimal controller synthesis with metric temporal logic specifications"
    - "cooperative planning for coupled multi agent systems under timed temporal specifications"
    - "time window temporal logic"

Connected through hyperedge HE-4 (induced by source paper "control from signal temporal logic specifications with smooth cumulative quantitative semantics"):
  with members:
    - "an milp approach for real time optimal controller synthesis with metric temporal logic specifications"
    - "robotic swarm control from spatio temporal specifications"
    - "temporal logic as filtering"
    - "on line monitoring for temporal logic robustness"

Connected through hyperedge HE-5 (induced by source paper "arithmetic geometric mean robustness for control from signal temporal logic specifications"):
  with members:
    - "an milp approach for real time optimal controller synthesis with metric temporal logic specifications"
    - "temporal logic as filtering"
    - "time window temporal logic"

Connected through hyperedge HE-6 (induced by source paper "reward design in cooperative multi agent reinforcement learning for packet routing"):
  with members:
    - "learning agent communication under limited bandwidth by message pruning"
    - "neighborhood cognition consistent multi agent reinforcement learning"
    - "learning multi agent communication under limited bandwidth restriction for internet packet routing"
    - "modelling the dynamic joint policy of teammates with attention multi agent ddpg"
    - "counterfactual multi agent policy gradients"
    - "analysing congestion problems in multi agent reinforcement learning"
    - "reinforcement mechanism design for e commerce"
    - "a distributional perspective on reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "falsification of cyber physical systems using deep reinforcement learning"):
  with members:
    - "an active learning approach to the falsification of black box cyber physical systems"
    - "asynchronous methods for deep reinforcement learning"
    - "continuous deep q learning with model based acceleration"
    - "reinforcement learning with temporal logic rewards"
    - "a learning based approach to control synthesis of markov decision processes for linear temporal logic specifications"
    - "on the robustness of temporal properties for stochastic models"
    - "mdp optimal control under 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.AI(Artificial Intelligence)