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: "deep imitative models for flexible inference planning and control"
  Abstract: Imitation learning provides an appealing framework for autonomous control: in many tasks, demonstrations of preferred behavior can be readily obtained from human experts, removing the need for costly and potentially dangerous online data collection in the real world. However, policies learned with imitation learning have limited flexibility to accommodate varied goals at test time. Model-based reinforcement learning (MBRL) offers considerably more flexibility, since a predictive model learned from data can be used to achieve various goals at test time. However, MBRL suffers from two shortcomings. First, the predictive model does not help to choose desired or safe outcomes -- it reasons only about what is possible, not what is preferred. Second, MBRL typically requires additional online data collection to ensure that the model is accurate in those situations that are actually encountered when attempting to achieve test time goals. Collecting this data with a partially trained model can be dangerous and time-consuming. In this paper, we aim to combine the benefits of imitation learning and MBRL, and propose imitative models: probabilistic predictive models able to plan expert-like trajectories to achieve arbitrary goals. We find this method substantially outperforms both direct imitation and MBRL in a simulated autonomous driving task, and can be learned efficiently from a fixed set of expert demonstrations without additional online data collection. We also show our model can flexibly incorporate user-supplied costs as test-time, can plan to sequences of goals, and can even perform well with imprecise goals, including goals on the wrong side of the road.

Connected through hyperedge HE-1 (induced by source paper "plop probabilistic polynomial objects trajectory planning for autonomous driving"):
  with members:
    - "trajectory forecasts in unknown environments conditioned on grid based plans"
    - "urban driving with conditional imitation learning"
    - "grip graph based interaction aware trajectory prediction"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "multiple futures prediction"
    - "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"
    - "conditional vehicle trajectories prediction in carla urban environment"
    - "on the variance of the adaptive learning rate and beyond"

Connected through hyperedge HE-2 (induced by source paper "multimodal end to end autonomous driving"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "towards self supervised high level sensor fusion"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "improved semantic stixels via multimodal sensor fusion"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "deep ordinal regression network for monocular depth estimation"
    - "pointpillars fast encoders for object detection from point clouds"
    - "cirl controllable imitative reinforcement learning for vision based self driving"

Connected through hyperedge HE-3 (induced by source paper "simulation based reinforcement learning for real world autonomous driving"):
  with members:
    - "model free deep reinforcement learning for urban autonomous driving"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "model based reinforcement learning for atari"
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "the limits and potentials of deep learning for robotics"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"

Connected through hyperedge HE-4 (induced by source paper "zero shot imitation learning from demonstrations for legged robot visual navigation"):
  with members:
    - "long range indoor navigation with prm rl"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "learning navigation subroutines from egocentric videos"
    - "universal planning networks"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "zero shot visual imitation"

Connected through hyperedge HE-5 (induced by source paper "conditional vehicle trajectories prediction in carla urban environment"):
  with members:
    - "convolutional social pooling for vehicle trajectory prediction"
    - "conditional affordance learning for driving in urban environments"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "end to end vehicle lateral control using a single fisheye camera"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "imitation learning for end to end vehicle longitudinal control with forward camera"

Connected through hyperedge HE-6 (induced by source paper "urban driving with conditional imitation learning"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "causal confusion in imitation learning"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "driving policy transfer via modularity and abstraction"

Connected through hyperedge HE-7 (induced by source paper "integration of imitation learning using gail and reinforcement learning using task achievement rewards via probabilistic generative model"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "variational inverse control with events a general framework for data driven reward definition"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "hierarchical imitation and reinforcement learning"
    - "maximum a posteriori policy optimisation"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "deeply aggrevated differentiable imitation learning for sequential prediction"
    - "overcoming exploration in reinforcement learning with demonstrations"

Connected through hyperedge HE-8 (induced by source paper "precog prediction conditioned on goals in visual multi agent settings"):
  with members:
    - "stochastic prediction of multi agent interactions from partial observations"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "generative hybrid representations for activity forecasting with no regret learning"
    - "an algorithmic perspective on imitation learning"
    - "generative modeling of multimodal multi human behavior"
    - "generative multi agent behavioral cloning"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"

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)