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: "imitating driver behavior with generative adversarial networks"
  Abstract: The ability to accurately predict and simulate human driving behavior is critical for the development of intelligent transportation systems. Traditional modeling methods have employed simple parametric models and behavioral cloning. This paper adopts a method for overcoming the problem of cascading errors inherent in prior approaches, resulting in realistic behavior that is robust to trajectory perturbations. We extend Generative Adversarial Imitation Learning to the training of recurrent policies, and we demonstrate that our model outperforms rule-based controllers and maximum likelihood models in realistic highway simulations. Our model both reproduces emergent behavior of human drivers, such as lane change rate, while maintaining realistic control over long time horizons.

Connected through hyperedge HE-1 (induced by source paper "generative adversarial imitation learning with neural networks global optimality and convergence rate"):
  with members:
    - "on computation and generalization of generative adversarial imitation learning"
    - "provably efficient reinforcement learning with linear function approximation"
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "an improved convergence analysis of stochastic variance reduced policy gradient"
    - "sample optimal parametric q learning using linearly additive features"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "on the global convergence of imitation learning a case for linear quadratic regulator"
    - "policy optimization provably converges to nash equilibria in zero sum linear quadratic games"

Connected through hyperedge HE-2 (induced by source paper "on computation and generalization of generative adversarial imitation learning"):
  with members:
    - "on the global convergence of imitation learning a case for linear quadratic regulator"
    - "learning temporal point processes via reinforcement learning"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "mmd gan towards deeper understanding of moment matching network"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "proximal policy optimization algorithms"
    - "generative adversarial imitation learning"
    - "guided cost learning deep inverse optimal control via policy optimization"

Connected through hyperedge HE-3 (induced by source paper "learning based distributionally robust motion control with gaussian processes"):
  with members:
    - "wasserstein distributionally robust motion control for collision avoidance using conditional value at risk"
    - "how should a robot assess risk towards an axiomatic theory of risk in robotics"
    - "end to end driving via conditional imitation learning"
    - "a real time model based reinforcement learning architecture for robot control"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-4 (induced by source paper "a probabilistic framework for imitating human race driver behavior"):
  with members:
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "model predictive control based trajectory generation for autonomous vehicles an architectural approach"
    - "generative adversarial imitation learning"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-5 (induced by source paper "energy based continuous inverse optimal control"):
  with members:
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "simulating emergent properties of human driving behavior using multi agent reward augmented imitation learning"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "a survey of inverse reinforcement learning challenges methods and progress"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "simplifying reward design through divide and conquer"

Connected through hyperedge HE-6 (induced by source paper "multiple futures prediction"):
  with members:
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "stochastic prediction of multi agent interactions from partial observations"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "trafficpredict trajectory prediction for heterogeneous traffic agents"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"

Connected through hyperedge HE-7 (induced by source paper "efficient black box assessment of autonomous vehicle safety"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "adaptive stress testing for autonomous vehicles"
    - "verifai a toolkit for the design and analysis of artificial intelligence based systems"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "fuzzergym a competitive framework for fuzzing and learning"
    - "deep imitative models for flexible inference planning and control"
    - "ai benchmark running deep neural networks on android smartphones"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"

Connected through hyperedge HE-8 (induced by source paper "topological navigation graph"):
  with members:
    - "an algorithmic perspective on imitation learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "semi parametric topological memory for navigation"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "learning to drive from simulation without real world labels"
    - "deep object centric policies for autonomous driving"
    - "on offline evaluation of vision based driving models"
    - "learning deployable navigation policies at kilometer scale from a single traversal"

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)