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: "simultaneous policy learning and latent state inference for imitating driver behavior"
  Abstract: In this work, we propose a method for learning driver models that account for variables that cannot be observed directly. When trained on a synthetic dataset, our models are able to learn encodings for vehicle trajectories that distinguish between four distinct classes of driver behavior. Such encodings are learned without any knowledge of the number of driver classes or any objective that directly requires the models to learn encodings for each class. We show that driving policies trained with knowledge of latent variables are more effective than baseline methods at imitating the driver behavior that they are trained to replicate. Furthermore, we demonstrate that the actions chosen by our policy are heavily influenced by the latent variable settings that are provided to them.

Connected through hyperedge HE-1 (induced by source paper "learning a multi modal policy via imitating demonstrations with mixed behaviors"):
  with members:
    - "deepmind control suite"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "hindsight experience replay"
    - "robust imitation of diverse behaviors"
    - "vision based multi task manipulation for inexpensive robots using end to end learning from demonstration"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "one shot imitation learning"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-2 (induced by source paper "learning temporal strategic relationships using generative adversarial imitation learning"):
  with members:
    - "tracking by prediction a deep generative model for mutli person localisation and tracking"
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "imitating driver behavior with generative adversarial networks"
    - "neural map structured memory for deep reinforcement learning"
    - "tree memory networks for modelling long term temporal dependencies"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"
    - "third person imitation learning"
    - "infogail interpretable imitation learning from visual demonstrations"

Connected through hyperedge HE-3 (induced by source paper "real time prediction of intermediate horizon automotive collision risk"):
  with members:
    - "accelerated evaluation of automated vehicles safety in lane change scenarios based on importance sampling techniques"
    - "imitating driver behavior with generative adversarial networks"
    - "the value of inferring the internal state of traffic participants for autonomous freeway driving"
    - "unified deep supervised domain adaptation and generalization"
    - "learning invariant feature spaces to transfer skills with reinforcement learning"
    - "sim to real robot learning from pixels with progressive nets"
    - "domain separation networks"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-4 (induced by source paper "burn in demonstrations for multi modal imitation learning"):
  with members:
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "imitating driver behavior with generative adversarial networks"
    - "robust imitation of diverse behaviors"
    - "towards deeper understanding of variational autoencoding models"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "one shot imitation learning"
    - "nips 2016 tutorial generative adversarial networks"
    - "generative adversarial imitation 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)