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: "evaluation of automated vehicles encountering pedestrians at unsignalized crossings"
  Abstract: Interactions between vehicles and pedestrians have always been a major problem in traffic safety. Experienced human drivers are able to analyze the environment and choose driving strategies that will help them avoid crashes. What is not yet clear, however, is how automated vehicles will interact with pedestrians. This paper proposes a new method for evaluating the safety and feasibility of the driving strategy of automated vehicles when encountering unsignalized crossings. MobilEye sensors installed on buses in Ann Arbor, Michigan, collected data on 2,973 valid crossing events. A stochastic interaction model was then created using a multivariate Gaussian mixture model. This model allowed us to simulate the movements of pedestrians reacting to an oncoming vehicle when approaching unsignalized crossings, and to evaluate the passing strategies of automated vehicles. A simulation was then conducted to demonstrate the evaluation procedure.

Connected through hyperedge HE-1 (induced by source paper "online monitoring for safe pedestrian vehicle interactions"):
  with members:
    - "numerical verification of affine systems with up to a billion dimensions"
    - "yolov3 an incremental improvement"
    - "probabilistically safe robot planning with confidence based human predictions"
    - "a scalable framework for real time multi robot multi human collision avoidance"
    - "dryvr data driven verification and compositional reasoning for automotive systems"

Connected through hyperedge HE-2 (induced by source paper "a hybrid control design for autonomous vehicles at uncontrolled intersections"):
  with members:
    - "autonomous vehicles that interact with pedestrians a survey of theory and practice"
    - "intent communication between autonomous vehicles and pedestrians"

Connected through hyperedge HE-3 (induced by source paper "pedestrian collision avoidance system for scenarios with occlusions"):
  with members:
    - "tackling occlusions limited sensor range with set based safety verification"

Connected through hyperedge HE-4 (induced by source paper "decomposition methods with deep corrections for reinforcement learning"):
  with members:
    - "autonomous braking system via deep reinforcement learning"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "prioritized experience replay"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"
    - "the complexity of decentralized control of markov decision processes"

Connected through hyperedge HE-5 (induced by source paper "people as sensors imputing maps from human actions"):
  with members:
    - "agreeing to cross how drivers and pedestrians communicate"
    - "joint attention in autonomous driving jaad"
    - "identifying modes of intent from driver behaviors in dynamic environments"
    - "experimental design for human in the loop driving simulations"

Connected through hyperedge HE-6 (induced by source paper "trafficnet an open naturalistic driving scenario library"):
  with members:
    - "accelerated evaluation of automated vehicles safety in lane change scenarios based on importance sampling techniques"
    - "accelerated evaluation of automated vehicles in car following maneuvers"
    - "evaluation of automated vehicles in the frontal cut in scenario an enhanced approach using piecewise mixture 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.MA(Multiagent Systems)