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: "distributed traffic signal control for maximum network throughput"
  Abstract: We propose a distributed algorithm for controlling traffic signals. Our algorithm is adapted from backpressure routing, which has been mainly applied to communication and power networks. We formally prove that our algorithm ensures global optimality as it leads to maximum network throughput even though the controller is constructed and implemented in a completely distributed manner. Simulation results show that our algorithm significantly outperforms SCATS, an adaptive traffic signal control system that is being used in many cities.

Connected through hyperedge HE-1 (induced by source paper "coordination of autonomous vehicles taxonomy and survey"):
  with members:
    - "arguing machines human supervision of black box ai systems that make life critical decisions"
    - "a market inspired approach for intersection management in urban road traffic networks"

Connected through hyperedge HE-2 (induced by source paper "internet of smart cameras for traffic lights optimization in smart cities"):
  with members:
    - "mask r cnn"

Connected through hyperedge HE-3 (induced by source paper "position weighted backpressure intersection control for urban networks"):
  with members:
    - "traffic state estimation using stochastic lagrangian dynamics"

Connected through hyperedge HE-4 (induced by source paper "softpressure a schedule driven backpressure algorithm for coping with network congestion"):
  with members:
    - "coping with large traffic volumes in schedule driven traffic signal control"

Connected through hyperedge HE-5 (induced by source paper "throughput optimal scheduling for multi hop networked transportation systems with switch over delay"):
  with members:
    - "towards provably safe mixed transportation systems with human driven and automated vehicles"
    - "back pressure traffic signal control with unknown routing rates"

Connected through hyperedge HE-6 (induced by source paper "connectivity aware traffic phase scheduling for heterogeneously connected vehicles"):
  with members:
    - "blocking avoidance in transportation systems"
    - "back pressure traffic signal control with unknown routing rates"

Connected through hyperedge HE-7 (induced by source paper "back pressure traffic signal control with unknown routing rates"):
  with members:
    - "capacity aware back pressure traffic signal control"

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)