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: "traffic network control from temporal logic specifications"
  Abstract: We propose a framework for generating a signal control policy for a traffic network of signalized intersections to accomplish control objectives expressible using linear temporal logic. By applying techniques from model checking and formal methods, we obtain a correct-by-construction controller that is guaranteed to satisfy complex specifications. To apply these tools, we identify and exploit structural properties particular to traffic networks that allow for efficient computation of a finite-state abstraction. In particular, traffic networks exhibit a  componentwise monotonicity  property which enables reaching set computations that scale linearly with the dimension of the continuous state space.

Connected through hyperedge HE-1 (induced by source paper "formal synthesis of control strategies for positive monotone systems"):
  with members:
    - "safety control of monotone systems with bounded uncertainties"
    - "robust temporal logic model predictive control"

Connected through hyperedge HE-2 (induced by source paper "cause mining and controller synthesis with stl"):
  with members:
    - "robust temporal logic model predictive control"

Connected through hyperedge HE-3 (induced by source paper "a policy search method for temporal logic specified reinforcement learning tasks"):
  with members:
    - "guided policy search as approximate mirror descent"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "q learning for robust satisfaction of signal temporal logic specifications"
    - "a provably correct mpc approach to safety control of urban traffic networks"
    - "concrete problems in ai safety"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "reinforcement learning with temporal logic rewards"
    - "unsupervised perceptual rewards for imitation learning"

Connected through hyperedge HE-4 (induced by source paper "safety control of monotone systems with bounded uncertainties"):
  with members:
    - "a provably correct mpc approach to safety control of urban traffic networks"
    - "mixed monotonicity of partial first in first out traffic flow models"

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)