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: "safe multi agent reinforcement learning for autonomous driving"
  Abstract: Autonomous driving is a multi-agent setting where the host vehicle must apply sophisticated negotiation skills with other road users when overtaking, giving way, merging, taking left and right turns and while pushing ahead in unstructured urban roadways. Since there are many possible scenarios, manually tackling all possible cases will likely yield a too simplistic policy. Moreover, one must balance between unexpected behavior of other drivers/pedestrians and at the same time not to be too defensive so that normal traffic flow is maintained. #R##N#In this paper we apply deep reinforcement learning to the problem of forming long term driving strategies. We note that there are two major challenges that make autonomous driving different from other robotic tasks. First, is the necessity for ensuring functional safety - something that machine learning has difficulty with given that performance is optimized at the level of an expectation over many instances. Second, the Markov Decision Process model often used in robotics is problematic in our case because of unpredictable behavior of other agents in this multi-agent scenario. We make three contributions in our work. First, we show how policy gradient iterations can be used without Markovian assumptions. Second, we decompose the problem into a composition of a Policy for Desires (which is to be learned) and trajectory planning with hard constraints (which is not learned). The goal of Desires is to enable comfort of driving, while hard constraints guarantees the safety of driving. Third, we introduce a hierarchical temporal abstraction we call an "Option Graph" with a gating mechanism that significantly reduces the effective horizon and thereby reducing the variance of the gradient estimation even further.

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 "review analyze and design a comprehensive deep reinforcement learning framework"):
  with members:
    - "deep reinforcement learning for cyber security"
    - "pytorch an imperative style high performance deep learning library"
    - "dopamine a research framework for deep reinforcement learning"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "large scale study of curiosity driven learning"
    - "accelerated methods for deep reinforcement learning"
    - "relational recurrent neural networks"
    - "distributed prioritized experience replay"

Connected through hyperedge HE-3 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium"):
  with members:
    - "provably efficient rl with rich observations via latent state decoding"
    - "provably efficient reinforcement learning with linear function approximation"
    - "exploration enhanced politex"
    - "emergent tool use from multi agent autocurricula"
    - "solving discounted stochastic two player games with near optimal time and sample complexity"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "sample optimal parametric q learning using linearly additive features"
    - "frequentist regret bounds for randomized least squares value iteration"

Connected through hyperedge HE-4 (induced by source paper "a visual communication map for multi agent deep reinforcement learning"):
  with members:
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "lenient multi agent deep reinforcement learning"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"
    - "adversarial advantage actor critic model for task completion dialogue policy learning"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "locally private distributed reinforcement learning"):
  with members:
    - "microscopic traffic simulation by cooperative multi agent deep reinforcement learning"
    - "collecting and analyzing multidimensional data with local differential privacy"
    - "local differential privacy for deep learning"
    - "negative update intervals in deep multi agent reinforcement learning"
    - "comparing population means under local differential privacy with significance and power"
    - "robust adversarial reinforcement learning"
    - "noisy networks for exploration"
    - "collecting telemetry data privately"

Connected through hyperedge HE-6 (induced by source paper "extended markov games to learn multiple tasks in multi agent reinforcement learning"):
  with members:
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "reinforcement learning for ltlf ldlf goals"
    - "hierarchical deep multiagent reinforcement learning"
    - "guided deep reinforcement learning for swarm systems"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "environment independent task specifications via gltl"
    - "safety aware apprenticeship learning"
    - "safe reinforcement learning via shielding"

Connected through hyperedge HE-7 (induced by source paper "a deep reinforcement learning based driving policy for autonomous road vehicles"):
  with members:
    - "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"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "limits of end to end learning"
    - "on a formal model of safe and scalable self driving cars"
    - "towards full automated drive in urban environments a demonstration in gomentum station california"
    - "brain inspired cognitive model with attention for self driving cars"
    - "end to end learning of driving models from large scale video datasets"

Connected through hyperedge HE-8 (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"
    - "navigating intersections with autonomous vehicles using deep reinforcement learning"
    - "end to end driving via conditional imitation learning"
    - "asynchronous methods for deep reinforcement learning"
    - "playing for data ground truth from computer games"

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)