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: "closing the gap towards end to end autonomous vehicle system"
  Abstract: Designing a driving policy for autonomous vehicles is a difficult task. Recent studies suggested an end-toend (E2E) training of a policy to predict car actuators directly from raw sensory inputs. It is appealing due to the ease of labeled data collection and since handcrafted features are avoided. Explicit drawbacks such as interpretability, safety enforcement and learning efficiency limit the practical application of the approach. In this paper, we amend the basic E2E architecture to address these shortcomings, while retaining the power of end-to-end learning. A key element in our proposed architecture is formulation of the learning problem as learning of trajectory. We also apply a Gaussian mixture model loss to contend with multi-modal data, and adopt a finance risk measure, conditional value at risk, to emphasize rare events. We analyze the effect of each concept and present driving performance in a highway scenario in the TORCS simulator. Video is available in this link: this https URL

Connected through hyperedge HE-1 (induced by source paper "explorations and lessons learned in building an autonomous formula sae car from simulations"):
  with members:
    - "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"
    - "learning end to end autonomous driving using guided auxiliary supervision"
    - "design of an autonomous racecar perception state estimation and system integration"
    - "virtual to real reinforcement learning for autonomous driving"

Connected through hyperedge HE-2 (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"
    - "learning accurate comfortable and human like driving"
    - "a multi stage multi task neural network for aerial scene interpretation and geolocalization"
    - "variational end to end navigation and localization"
    - "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"

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.RO(Robotics)