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: "dissipation of stop and go waves via control of autonomous vehicles field experiments"
  Abstract: Abstract   Traffic waves are phenomena that emerge when the vehicular density exceeds a critical threshold. Considering the presence of increasingly automated vehicles in the traffic stream, a number of research activities have focused on the influence of automated vehicles on the bulk traffic flow. In the present article, we demonstrate experimentally that intelligent control of an autonomous vehicle is able to dampen stop-and-go waves that can arise even in the absence of geometric or lane changing triggers. Precisely, our experiments on a circular track with more than 20 vehicles show that traffic waves emerge consistently, and that they can be dampened by controlling the velocity of a single vehicle in the flow. We compare metrics for velocity, braking events, and fuel economy across experiments. These experimental findings suggest a paradigm shift in traffic management: flow control will be possible via a few mobile actuators (less than 5%) long before a majority of vehicles have autonomous capabilities.

Connected through hyperedge HE-1 (induced by source paper "watch your back backdoor attacks in deep reinforcement learning based autonomous vehicle control systems"):
  with members:
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "backdoor embedding in convolutional neural network models via invisible perturbation"
    - "spectral signatures in backdoor attacks"
    - "sequential attacks on agents for long term adversarial goals"
    - "adversarial attacks on neural network policies"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "flow a modular learning framework for autonomy in traffic"
    - "vulnerability of deep reinforcement learning to policy induction attacks"

Connected through hyperedge HE-2 (induced by source paper "zero shot autonomous vehicle policy transfer from simulation to real world via adversarial learning"):
  with members:
    - "demonstration of a time efficient mobility system using a scaled smart city"
    - "control of connected and automated vehicles state of the art and future challenges"
    - "simulation to scaled city zero shot policy transfer for traffic control via autonomous vehicles"
    - "flow a modular learning framework for autonomy in traffic"
    - "expert level control of ramp metering based on multi task deep reinforcement learning"
    - "robust adversarial reinforcement learning"
    - "rllib abstractions for distributed reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-3 (induced by source paper "keeping calm when distributed consistency is easy"):
  with members:
    - "blazes coordination analysis for distributed programs"
    - "communication steps for parallel query processing"
    - "relational transducers for declarative networking"
    - "building on quicksand"

Connected through hyperedge HE-4 (induced by source paper "pricing traffic networks with mixed vehicle autonomy"):
  with members:
    - "how will the presence of autonomous vehicles affect the equilibrium state of traffic networks"
    - "a game theoretic macroscopic model of bypassing at traffic diverges with applications to mixed autonomy networks"

Connected through hyperedge HE-5 (induced by source paper "how will the presence of autonomous vehicles affect the equilibrium state of traffic networks"):
  with members:
    - "a game theoretic macroscopic model of bypassing at traffic diverges with applications to mixed autonomy networks"
    - "measuring impact of adaptive and cooperative adaptive cruise control on throughput of signalized intersections"

Connected through hyperedge HE-6 (induced by source paper "model based string stability of adaptive cruise control systems using field data"):
  with members:
    - "string stability and a delay based spacing policy for vehicle platoons subject to disturbances"

Connected through hyperedge HE-7 (induced by source paper "m2m billing for electric autonomous vehicles"):
  with members:
    - "towards blockchain based robonomics autonomous agents behavior validation"
    - "microservices how to make your application scale"
    - "jolie coog buildings internet of things for smart building infrastructure supporting concurrent apps utilizing distibuted microservices"

Connected through hyperedge HE-8 (induced by source paper "an architecture for distributed ledger based m2m auditing for electric autonomous vehicles"):
  with members:
    - "towards blockchain based robonomics autonomous agents behavior validation"
    - "microservices how to make your application scale"
    - "jolie coog buildings internet of things for smart building infrastructure supporting concurrent apps utilizing distibuted microservices"

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)