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: "q learning for robust satisfaction of signal temporal logic specifications"
  Abstract: This paper addresses the problem of learning optimal policies for satisfying signal temporal logic (STL) specifications by agents with unknown stochastic dynamics. The system is modeled as a Markov decision process, in which the states represent partitions of a continuous space and the transition probabilities are unknown. We formulate two synthesis problems where the desired STL specification is enforced by maximizing the probability of satisfaction, and the expected robustness degree, that is, a measure quantifying the quality of satisfaction. We discuss that Q-learning is not directly applicable to these problems because, based on the quantitative semantics of STL, the probability of satisfaction and expected robustness degree are not in the standard objective form of Q-learning. To resolve this issue, we propose an approximation of STL synthesis problems that can be solved via Q-learning, and we derive some performance bounds for the policies obtained by the approximate approach. The performance of the proposed method is demonstrated via simulations.

Connected through hyperedge HE-1 (induced by source paper "tractable reinforcement learning of signal temporal logic objectives"):
  with members:
    - "temporal logic guided safe reinforcement learning using control barrier functions"
    - "transfer of temporal logic formulas in reinforcement learning"
    - "reinforcement learning with temporal logic rewards"
    - "on line monitoring for temporal logic robustness"
    - "verification of markov decision processes using learning algorithms"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "temporal logic guided safe reinforcement learning using control barrier functions"):
  with members:
    - "barrier certified adaptive reinforcement learning with applications to brushbot navigation"
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"
    - "automata guided reinforcement learning with demonstrations"
    - "safe exploration in continuous action spaces"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "reinforcement learning for ltlf ldlf goals"

Connected through hyperedge HE-3 (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"
    - "a policy search method for temporal logic specified reinforcement learning tasks"
    - "probabilistic motion planning under temporal tasks and soft constraints"
    - "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-4 (induced by source paper "constraint satisfaction propagation non stationary policy synthesis for temporal logic planning"):
  with members:
    - "optimizing agent behavior over long time scales by transporting value"
    - "temporal logic control of general markov decision processes by approximate policy refinement"
    - "rudder return decomposition for delayed rewards"
    - "variational option discovery algorithms"
    - "eigenoption discovery through the deep successor representation"
    - "the eigenoption critic framework"
    - "hierarchical subtask discovery with non negative matrix factorization"
    - "environment independent task specifications via gltl"

Connected through hyperedge HE-5 (induced by source paper "using logical specifications of objectives in multi objective reinforcement learning"):
  with members:
    - "using natural language for reward shaping in reinforcement learning"
    - "temporal logic guided safe reinforcement learning using control barrier functions"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "dynamic weights in multi objective deep reinforcement learning"
    - "soft actor critic algorithms and applications"
    - "generalizing across multi objective reward functions in deep reinforcement learning"
    - "hindsight experience replay"
    - "reinforcement learning with temporal logic rewards"

Connected through hyperedge HE-6 (induced by source paper "control of mobile robots using barrier functions under temporal logic specifications"):
  with members:
    - "an optimal task allocation strategy for heterogeneous multi robot systems"
    - "prescribed performance control guided policy improvement for satisfying signal temporal logic tasks"
    - "control of multi agent systems with finite time control barrier certificates and temporal logic"
    - "safe certificate based maneuvers for teams of quadrotors using differential flatness"
    - "distributed communication aware motion planning for multi agent systems from stl and spatel specifications"
    - "safety barrier certificates for heterogeneous multi robot systems"
    - "the robotarium a remotely accessible swarm robotics research testbed"
    - "automata theory meets barrier certificates temporal logic verification of nonlinear systems"

Connected through hyperedge HE-7 (induced by source paper "transfer of temporal logic formulas in reinforcement learning"):
  with members:
    - "information guided temporal logic inference with prior knowledge"
    - "mining parametric temporal logic properties in model based design for cyber physical systems"
    - "census signal temporal logic inference for multiagent group behavior analysis"
    - "learning linear temporal properties"
    - "learning task specifications from demonstrations"
    - "safe reinforcement learning via shielding"
    - "reinforcement learning with temporal logic rewards"
    - "probably approximately correct mdp learning and control with temporal logic constraints"

Connected through hyperedge HE-8 (induced by source paper "planning with uncertain specifications puns"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "planning with state abstractions for non markovian task specifications"
    - "simplifying reward design through divide and conquer"
    - "environment independent task specifications via gltl"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "inverse reward design"

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.SY(Systems and Control)