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: "learning end to end autonomous driving using guided auxiliary supervision"
  Abstract: Learning to drive faithfully in highly stochastic urban settings remains an open problem. To that end, we propose a Multi-task Learning from Demonstration (MT-LfD) framework which uses supervised auxiliary task prediction to guide the main task of predicting the driving commands. Our framework involves an end-to-end trainable network for imitating the expert demonstrator's driving commands. The network intermediately predicts visual affordances and action primitives through direct supervision which provide the aforementioned auxiliary supervised guidance. We demonstrate that such joint learning and supervised guidance facilitates hierarchical task decomposition, assisting the agent to learn faster, achieve better driving performance and increases transparency of the otherwise black-box end-to-end network. We run our experiments to validate the MT-LfD framework in CARLA, an open-source urban driving simulator. We introduce multiple non-player agents in CARLA and induce temporal noise in them for realistic stochasticity.

Connected through hyperedge HE-1 (induced by source paper "efficient motion planning for automated lane change based on imitation learning and mixed integer optimization"):
  with members:
    - "a data driven method of optimizing feedforward compensator for autonomous vehicle"
    - "self driving cars a survey"
    - "learning a lattice planner control set for autonomous vehicles"
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "a fast integrated planning and control framework for autonomous driving via imitation learning"
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"

Connected through hyperedge HE-2 (induced by source paper "end to end model free reinforcement learning for urban driving using implicit affordances"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "is deep reinforcement learning really superhuman on atari leveling the playing field"
    - "learning by cheating"
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "end to end race driving with deep reinforcement learning"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "conditional affordance learning for driving in urban environments"

Connected through hyperedge HE-3 (induced by source paper "closing the gap towards end to end autonomous vehicle system"):
  with members:
    - "deep density networks and uncertainty in recommender systems"
    - "end to end multi modal multi task vehicle control for self driving cars with visual perception"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "focal loss for dense object detection"
    - "failures of gradient based deep learning"
    - "on a formal model of safe and scalable self driving cars"
    - "end to end driving via conditional imitation learning"
    - "an overview of multi task learning in deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "explorations and lessons learned in building an autonomous formula sae car from simulations"):
  with members:
    - "closing the gap towards end to end autonomous vehicle system"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "real time dynamic object detection for autonomous driving using prior 3d maps"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "design of an autonomous racecar perception state estimation and system integration"
    - "virtual to real reinforcement learning for autonomous driving"

Connected through hyperedge HE-5 (induced by source paper "safer end to end autonomous driving via conditional imitation learning and command augmentation"):
  with members:
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "driving policy transfer via modularity and abstraction"
    - "learning to drive in a day"
    - "carla an open urban driving simulator"
    - "on a formal model of safe and scalable self driving cars"
    - "end to end learning for self driving cars"
    - "building machines that learn and think like people"

Connected through hyperedge HE-6 (induced by source paper "learning to drive from simulation without real world labels"):
  with members:
    - "video to video synthesis"
    - "vr goggles for robots real to sim domain adaptation for visual control"
    - "multimodal unsupervised image to image translation"
    - "driving policy transfer via modularity and abstraction"
    - "learning to drive in a day"
    - "on offline evaluation of vision based driving models"
    - "semi convolutional operators for instance segmentation"
    - "modular vehicle control for transferring semantic information to unseen weather conditions using gans"

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)