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: "navigating intersections with autonomous vehicles using deep reinforcement learning"
  Abstract: Providing an efficient strategy to navigate safely through unsignaled intersections is a difficult task that requires determining the intent of other drivers. We explore the effectiveness of Deep Reinforcement Learning to handle intersection problems. Using recent advances in Deep RL, we are able to learn policies that surpass the performance of a commonly-used heuristic approach in several metrics including task completion time and goal success rate and have limited ability to generalize. We then explore a system's ability to learn active sensing behaviors to enable navigating safely in the case of occlusions. Our analysis, provides insight into the intersection handling problem, the solutions learned by the network point out several shortcomings of current rule-based methods, and the failures of our current deep reinforcement learning system point to future research directions.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for autonomous driving a survey"):
  with members:
    - "yes we gan applying adversarial techniques for autonomous driving"
    - "multi objective multi agent decision making a utility based analysis and survey"
    - "model free deep reinforcement learning for urban autonomous driving"
    - "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"
    - "soilingnet soiling detection on automotive surround view cameras"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "rlpyt a research code base for deep reinforcement learning in pytorch"
    - "on first order meta learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "intelligent roundabout insertion using deep reinforcement learning"):
  with members:
    - "an introduction to deep reinforcement learning"
    - "automated speed and lane change decision making using deep reinforcement learning"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "carla an open urban driving simulator"
    - "end to end driving via conditional imitation learning"
    - "asynchronous methods for deep reinforcement learning"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "playing for data ground truth from computer games"

Connected through hyperedge HE-3 (induced by source paper "model free vision based shaping of deformable plastic materials"):
  with members:
    - "mis slam real time large scale dense deformable slam system in minimal invasive surgery based on heterogeneous computing"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"
    - "learning robust bed making using deep imitation learning with dart"
    - "active perception and modeling of deformable surfaces using gaussian processes and position based dynamics"
    - "deep reinforcement learning in large discrete action spaces"

Connected through hyperedge HE-4 (induced by source paper "microscopic traffic simulation by cooperative multi agent deep reinforcement learning"):
  with members:
    - "automated speed and lane change decision making using deep reinforcement learning"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "elements of effective deep reinforcement learning towards tactical driving decision making"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "towards full automated drive in urban environments a demonstration in gomentum station california"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "end to end learning for self driving cars"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "game theoretic modeling of traffic in unsignalized intersection network for autonomous vehicle control verification and validation"):
  with members:
    - "a game theoretic approach to decision making for multiple vehicles at roundabout"
    - "game theoretic modeling of multi vehicle interactions at uncontrolled intersections"
    - "decision making for autonomous vehicles at unsignalized intersection in presence of malicious vehicles"
    - "using control synthesis to generate corner cases a case study on autonomous driving"
    - "hierarchical game theoretic planning for autonomous vehicles"
    - "adaptive game theoretic decision making for autonomous vehicle control at roundabouts"
    - "carla an open urban driving simulator"
    - "belief state planning for autonomously navigating urban intersections"

Connected through hyperedge HE-6 (induced by source paper "controlling an autonomous vehicle with deep reinforcement learning"):
  with members:
    - "learning to drive in a day"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "proximal policy optimization algorithms"
    - "end to end learning for self driving cars"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "sample efficient actor critic with experience replay"
    - "asynchronous methods for deep reinforcement learning"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"

Connected through hyperedge HE-7 (induced by source paper "longitudinal dynamic versus kinematic models for car following control using deep reinforcement learning"):
  with members:
    - "autonomous ramp merge maneuver based on reinforcement learning with continuous action space"
    - "urban driving with multi objective deep reinforcement learning"
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "adaptive behavior generation for autonomous driving using deep reinforcement learning with compact semantic states"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "distributed distributional deterministic policy gradients"
    - "learning values across many orders of magnitude"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "safe reinforcement learning with scene decomposition for navigating complex urban environments"):
  with members:
    - "reinforcement learning with probabilistic guarantees for autonomous driving"
    - "learning negotiating behavior between cars in intersections using deep q learning"
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "deep variational reinforcement learning for pomdps"
    - "utility decomposition with deep corrections for scalable planning under uncertainty"
    - "belief state planning for autonomously navigating urban intersections"
    - "autonomous braking system via deep reinforcement learning"
    - "safe reinforcement learning via shielding"

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.AI(Artificial Intelligence)