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 market inspired approach for intersection management in urban road traffic networks"
  Abstract: Traffic congestion in urban road networks is a costly problem that affects all major cities in developed countries. To tackle this problem, it is possible (i) to act on the supply side, increasing the number of roads or lanes in a network, (ii) to reduce the demand, restricting the access to urban areas at specific hours or to specific vehicles, or (iii) to improve the efficiency of the existing network, by means of a widespread use of so-called Intelligent Transportation Systems (ITS). In line with the recent advances in smart transportation management infrastructures, ITS has turned out to be a promising field of application for artificial intelligence techniques. In particular, multiagent systems seem to be the ideal candidates for the design and implementation of ITS. In fact, drivers can be naturally modelled as autonomous agents that interact with the transportation management infrastructure, thereby generating a large-scale, open, agent-based system. To regulate such a system and maintain a smooth and efficient flow of traffic, decentralised mechanisms for the management of the transportation infrastructure are needed.#R##N##R##N#In this article we propose a distributed, market-inspired, mechanism for the management of a future urban road network, where intelligent autonomous vehicles, operated by software agents on behalf of their human owners, interact with the infrastructure in order to travel safely and efficiently through the road network. Building on the reservationbased intersection control model proposed by Dresner and Stone, we consider two different scenarios: one with a single intersection and one with a network of intersections. In the former, we analyse the performance of a novel policy based on combinatorial auctions for the allocation of reservations. In the latter, we analyse the impact that a traffic assignment strategy inspired by competitive markets has on the drivers' route choices. Finally we propose an adaptive management mechanism that integrates the auction-based traffic control policy with the competitive traffic assignment strategy.

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"
    - "distributed traffic signal control for maximum network throughput"

Connected through hyperedge HE-2 (induced by source paper "optimal mechanisms for distributed resource allocation"):
  with members:
    - "tight approximation bounds for maximum multi coverage"
    - "optimal price of anarchy in cost sharing games"
    - "distributed resource allocation through utility design part i optimizing the performance certificates via the price of anarchy"
    - "intense competition can drive selfish explorers to optimize coverage"
    - "distributed resource allocation through utility design part ii applications to submodular supermodular and set covering problems"
    - "multiagent maximum coverage problems the trade off between anarchy and stability"
    - "learning in games robustness of fast convergence"
    - "a unifying tool for bounding the quality of non cooperative solutions in weighted congestion games"

Connected through hyperedge HE-3 (induced by source paper "social cost guarantees in smart route guidance"):
  with members:
    - "truthful facility assignment with resource augmentation an exact analysis of serial dictatorship"
    - "a compartmental model for traffic networks and its dynamical behavior"
    - "social welfare in one sided matchings random priority and beyond"
    - "approximately optimal mechanism design via differential privacy"

Connected through hyperedge HE-4 (induced by source paper "safe and efficient intersection control of connected and autonomous intersection traffic"):
  with members:
    - "autonomous and connected intersection crossing traffic management using discrete time occupancies trajectory"
    - "power traffic coordinated operation for bi peak shaving and bi ramp smoothing a hierarchical data driven approach"
    - "chance constrained day ahead hourly scheduling in distribution system operation"
    - "load forecasting based distribution system network reconfiguration a distributed data driven approach"
    - "polling systems based autonomous vehicle coordination in traffic intersections with no traffic signals"

Connected through hyperedge HE-5 (induced by source paper "autonomous and connected intersection crossing traffic management using discrete time occupancies trajectory"):
  with members:
    - "action recognition based on joint trajectory maps using convolutional neural networks"
    - "polling systems based autonomous vehicle coordination in traffic intersections with no traffic signals"

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.GT(Computer Science and Game Theory)