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: "following high level navigation instructions on a simulated quadcopter with imitation learning"
  Abstract: We introduce a method for following high-level navigation instructions by mapping directly from images, instructions and pose estimates to continuous low-level velocity commands for real-time control. The Grounded Semantic Mapping Network (GSMN) is a fully-differentiable neural network architecture that builds an explicit semantic map in the world reference frame by incorporating a pinhole camera projection model within the network. The information stored in the map is learned from experience, while the local-to-world transformation is computed explicitly. We train the model using DAggerFM, a modified variant of DAgger that trades tabular convergence guarantees for improved training speed and memory use. We test GSMN in virtual environments on a realistic quadcopter simulator and show that incorporating an explicit mapping and grounding modules allows GSMN to outperform strong neural baselines and almost reach an expert policy performance. Finally, we analyze the learned map representations and show that using an explicit map leads to an interpretable instruction-following model.

Connected through hyperedge HE-1 (induced by source paper "affect based intrinsic rewards for exploration and learning"):
  with members:
    - "monocular plan view networks for autonomous driving"
    - "explorations and lessons learned in building an autonomous formula sae car from simulations"
    - "learning exploration policies for navigation"
    - "large scale study of curiosity driven learning"
    - "visceral machines risk aversion in reinforcement learning with intrinsic physiological rewards"
    - "on learning intrinsic rewards for policy gradient methods"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "episodic curiosity through reachability"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning based visual navigation with information theoretic regularization"):
  with members:
    - "learning a controller fusion network by online trajectory filtering for vision based uav racing"
    - "sim real joint reinforcement transfer for 3d indoor navigation"
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "bayesian relational memory for semantic visual navigation"
    - "reinforcement learning from imperfect demonstrations"
    - "deep drone racing learning agile flight in dynamic environments"
    - "a real time game theoretic planner for autonomous two player drone racing"
    - "oil observational imitation learning"

Connected through hyperedge HE-3 (induced by source paper "learning to map natural language instructions to physical quadcopter control using simulated flight"):
  with members:
    - "stay on the path instruction fidelity in vision and language navigation"
    - "executing instructions in situated collaborative interactions"
    - "chasing ghosts instruction following as bayesian state tracking"
    - "universal planning networks"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "speaker follower models for vision and language navigation"
    - "situated mapping of sequential instructions to actions with single step reward observation"
    - "mapping instructions to actions in 3d environments with visual goal prediction"

Connected through hyperedge HE-4 (induced by source paper "executing instructions in situated collaborative interactions"):
  with members:
    - "meetup a corpus of joint activity dialogues in a visual environment"
    - "hierarchical decision making by generating and following natural language instructions"
    - "a natural language corpus of common grounding under continuous and partially observable context"
    - "learning to map context dependent sentences to executable formal queries"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "decoupling strategy and generation in negotiation dialogues"
    - "situated mapping of sequential instructions to actions with single step reward observation"

Connected through hyperedge HE-5 (induced by source paper "conditional driving from natural language instructions"):
  with members:
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "prospection interpretable plans from language by predicting the future"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "follownet robot navigation by following natural language directions with deep reinforcement learning"
    - "driving policy transfer via modularity and abstraction"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-6 (induced by source paper "affect based intrinsic rewards for learning general representations"):
  with members:
    - "monocular plan view networks for autonomous driving"
    - "learning exploration policies for navigation"
    - "large scale study of curiosity driven learning"
    - "visceral machines risk aversion in reinforcement learning with intrinsic physiological rewards"
    - "on learning intrinsic rewards for policy gradient methods"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "episodic curiosity through reachability"
    - "virtual to real reinforcement learning for autonomous driving"

Connected through hyperedge HE-7 (induced by source paper "graph attention memory for visual navigation"):
  with members:
    - "a behavioral approach to visual navigation with graph localization networks"
    - "learning exploration policies for navigation"
    - "zero shot visual imitation"
    - "semi parametric topological memory for navigation"
    - "reinforcement learning and deep learning based lateral control for autonomous driving"
    - "learning to navigate in cities without a map"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "unifying map and landmark based representations for visual navigation"

Connected through hyperedge HE-8 (induced by source paper "learning a controller fusion network by online trajectory filtering for vision based uav racing"):
  with members:
    - "deep drone racing learning agile flight in dynamic environments"
    - "a real time game theoretic planner for autonomous two player drone racing"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "teaching uavs to race end to end regression of agile controls in simulation"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "agile autonomous driving using end to end deep imitation learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "cad2rl real single image flight without a single real image"

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)