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: "variational end to end navigation and localization"
  Abstract: Deep learning has revolutionized the ability to learn "end-to-end" autonomous vehicle control directly from raw sensory data. While there have been recent extensions to handle forms of navigation instruction, these works are unable to capture the full distribution of possible actions that could be taken and to reason about localization of the robot within the environment. In this paper, we extend end-to-end driving networks with the ability to perform point-to-point navigation as well as probabilistic localization using only noisy GPS data. We define a novel variational network capable of learning from raw camera data of the environment as well as higher level roadmaps to predict (1) a full probability distribution over the possible control commands; and (2) a deterministic control command capable of navigating on the route specified within the map. Additionally, we formulate how our model can be used to localize the robot according to correspondences between the map and the observed visual road topology, inspired by the rough localization that human drivers can perform. We test our algorithms on real-world driving data that the vehicle has never driven through before, and integrate our point-to-point navigation algorithms onboard a full-scale autonomous vehicle for real-time performance. Our localization algorithm is also evaluated over a new set of roads and intersections to demonstrates rough pose localization even in situations without any GPS prior.

Connected through hyperedge HE-1 (induced by source paper "probabilistic future prediction for video scene understanding"):
  with members:
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "model based reinforcement learning for atari"
    - "adversarial video generation on complex datasets"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning latent dynamics for planning from pixels"
    - "learning plannable representations with causal infogan"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"

Connected through hyperedge HE-2 (induced by source paper "learning to walk in the real world with minimal human effort"):
  with members:
    - "safety guided deep reinforcement learning via online gaussian process estimation"
    - "solving rubik s cube with a robot hand"
    - "generalizing from a few environments in safety critical reinforcement learning"
    - "data efficient reinforcement learning for legged robots"
    - "deepgait planning and control of quadrupedal gaits using deep reinforcement learning"
    - "norml no reward meta learning"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "learning agile and dynamic motor skills for legged robots"

Connected through hyperedge HE-3 (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"
    - "driving policy transfer via modularity and abstraction"

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"
    - "laser map aided visual inertial localization in changing environment"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "intention net integrating planning and deep learning for goal directed autonomous navigation"
    - "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"
    - "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"
    - "intention net integrating planning and deep learning for goal directed autonomous navigation"
    - "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 "you are here geolocation by embedding maps and images"):
  with members:
    - "towards navigation without precise localization weakly supervised learning of goal directed navigation cost map"
    - "automated map reading image based localisation in 2 d maps using binary semantic descriptors"
    - "in defense of the triplet loss for person re identification"
    - "learning from maps visual common sense for autonomous driving"
    - "find your way by observing the sun and other semantic cues"
    - "localizing and orienting street views using overhead imagery"
    - "semantic image based geolocation given a map"
    - "learning deep structure preserving image text embeddings"

Connected through hyperedge HE-7 (induced by source paper "learning navigation by visual localization and trajectory prediction"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "closing the gap towards end to end autonomous vehicle system"
    - "learning accurate comfortable and human like driving"
    - "a multi stage multi task neural network for aerial scene interpretation and geolocalization"
    - "learning to navigate in cities without a map"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "geometric loss functions for camera pose regression with deep learning"
    - "unsupervised learning from video to detect foreground objects in single images"

Connected through hyperedge HE-8 (induced by source paper "uncertainty aware driver trajectory prediction at urban intersections"):
  with members:
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "failing to learn autonomously identifying perception failures for self driving cars"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "autorally an open platform for aggressive autonomous driving"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network"
    - "very deep convolutional networks for large scale image recognition"

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)