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: "multinet multi modal multi task learning for autonomous driving"
  Abstract: Several deep learning approaches have been applied to the autonomous driving task, many employing end-to-end deep neural networks. Autonomous driving is complex, utilizing multiple behavioral modalities ranging from lane changing to turning and stopping. However, most existing approaches do not factor in the different behavioral modalities of the driving task into the training strategy. This paper describes a technique for using Multi-Modal Multi-Task Learning, which we denote as MultiNet which considers multiple behavioral modalities as distinct modes of operation for an end-to-end autonomous deep neural network utilizing the insertion of modal information as secondary input data. Using labeled data from hours of driving our fleet of 1/10th scale model cars, we trained different neural networks to imitate the steering angle and driving speed of human control of a car. We show that in each case, MultiNet models outperform networks trained on individual tasks, while using a fraction of the number of parameters.

Connected through hyperedge HE-1 (induced by source paper "multi objective autonomous braking system using naturalistic dataset"):
  with members:
    - "analysis of distracted pedestrians waiting time head mounted immersive virtual reality application"
    - "unity a general platform for intelligent agents"
    - "virtual immersive reality for stated preference travel behaviour experiments a case study of autonomous vehicles on urban roads"
    - "carla an open urban driving simulator"
    - "autonomous braking system via deep reinforcement learning"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "proximal policy optimization algorithms"
    - "continuous control with deep reinforcement learning"

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.LG(Machine Learning)