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: "intention net integrating planning and deep learning for goal directed autonomous navigation"
  Abstract: How can a delivery robot navigate reliably to a destination in a new office building, with minimal prior information? To tackle this challenge, this paper introduces a two-level hierarchical approach, which integrates model-free deep learning and model-based path planning. At the low level, a neural-network motion controller, called the intention-net, is trained end-to-end to provide robust local navigation. The intention-net maps images from a single monocular camera and "intentions" directly to robot controls. At the high level, a path planner uses a crude map, e.g., a 2-D floor plan, to compute a path from the robot's current location to the goal. The planned path provides intentions to the intention-net. Preliminary experiments suggest that the learned motion controller is robust against perceptual uncertainty and by integrating with a path planner, it generalizes effectively to new environments and goals.

Connected through hyperedge HE-1 (induced by source paper "social navigation with human empowerment driven reinforcement learning"):
  with members:
    - "deep local trajectory replanning and control for robot navigation"
    - "a survey on intrinsic motivation in reinforcement learning"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "emi exploration with mutual information"
    - "socially aware motion planning with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "combining optimal control and learning for visual navigation in novel environments"):
  with members:
    - "a behavioral approach to visual navigation with graph localization networks"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "neural autonomous navigation with riemannian motion policy"
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "deep drone racing learning agile flight in dynamic environments"
    - "learning navigation behaviors end to end with autorl"
    - "semi parametric topological memory for navigation"
    - "variational end to end navigation and localization"

Connected through hyperedge HE-3 (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"
    - "deep imitative models for flexible inference planning and control"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "deepgoal learning to drive with driving intention from human control demonstration"):
  with members:
    - "yolov3 an incremental improvement"
    - "conditional affordance learning for driving in urban environments"
    - "variational end to end navigation and localization"
    - "laser map aided visual inertial localization in changing environment"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "3d ssd learning hierarchical features from rgb d images for amodal 3d object detection"
    - "end to end driving via conditional imitation learning"
    - "learning from maps visual common sense for autonomous driving"

Connected through hyperedge HE-5 (induced by source paper "towards navigation without precise localization weakly supervised learning of goal directed navigation cost map"):
  with members:
    - "yolov3 an incremental improvement"
    - "variational end to end navigation and localization"
    - "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
    - "laser map aided visual inertial localization in changing environment"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "ground edge based lidar localization without a reflectivity calibration for autonomous driving"
    - "3d ssd learning hierarchical features from rgb d images for amodal 3d object detection"
    - "robust and precise vehicle localization based on multi sensor fusion in diverse city scenes"

Connected through hyperedge HE-6 (induced by source paper "mapless navigation among dynamics with social safety awareness a reinforcement learning approach from 2d laser scans"):
  with members:
    - "a behavioral approach to visual navigation with graph localization networks"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "curiosity driven exploration for mapless navigation with deep reinforcement learning"
    - "crowdmove autonomous mapless navigation in crowded scenarios"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "learning end to end multimodal sensor policies for autonomous navigation"

Connected through hyperedge HE-7 (induced by source paper "fully convolutional search heuristic learning for rapid path planners"):
  with members:
    - "learning heuristic search via imitation"
    - "cognitive mapping and planning for visual navigation"
    - "watch this scalable cost function learning for path planning in urban environments"
    - "potential functions based sampling heuristic for optimal path planning"
    - "cnn architectures for large scale audio classification"
    - "value iteration networks"
    - "multi scale context aggregation by dilated convolutions"
    - "maximum entropy deep inverse reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "learning navigation behaviors end to end with autorl"):
  with members:
    - "barc backward reachability curriculum for robotic reinforcement learning"
    - "automated curriculum learning for neural networks"
    - "large scale evolution of image classifiers"
    - "reverse curriculum generation for reinforcement learning"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "prm rl long range robotic navigation tasks by combining reinforcement learning and sampling based planning"
    - "learning to reinforcement learn"
    - "learning to learn by gradient descent by gradient descent"

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)