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: "end to end multi modal multi task vehicle control for self driving cars with visual perception"
  Abstract: Convolutional Neural Networks (CNN) have been successfully applied to autonomous driving tasks, many in an end-to-end manner. Previous end-to-end steering control methods take an image or an image sequence as the input and directly predict the steering angle with CNN. Although single task learning on steering angles has reported good performances, the steering angle alone is not sufficient for vehicle control. In this work, we propose a multi-task learning framework to predict the steering angle and speed control simultaneously in an end-to-end manner. Since it is nontrivial to predict accurate speed values with only visual inputs, we first propose a network to predict discrete speed commands and steering angles with image sequences. Moreover, we propose a multi-modal multi-task network to predict speed values and steering angles by taking previous feedback speeds and visual recordings as inputs. Experiments are conducted on the public Udacity dataset and a newly collected SAIC dataset. Results show that the proposed model predicts steering angles and speed values accurately. Furthermore, we improve the failure data synthesis methods to solve the problem of error accumulation in real road tests.

Connected through hyperedge HE-1 (induced by source paper "closing the gap towards end to end autonomous vehicle system"):
  with members:
    - "learning end to end autonomous driving using guided auxiliary supervision"
    - "deep density networks and uncertainty in recommender systems"
    - "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-2 (induced by source paper "a systematic comparison of deep learning architectures in an autonomous vehicle"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "citypersons a diverse dataset for pedestrian detection"
    - "beyond grand theft auto v for training testing and enhancing deep learning in self driving cars"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "l2 regularization versus batch and weight normalization"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-3 (induced by source paper "evaluating uncertainty quantification in end to end autonomous driving control"):
  with members:
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "end to end learning for self driving cars"
    - "modelling uncertainty in deep learning for camera relocalization"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "visualizing and understanding convolutional networks"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-4 (induced by source paper "imitation learning for end to end vehicle longitudinal control with forward camera"):
  with members:
    - "end to end vehicle lateral control using a single fisheye camera"
    - "end to end learning for self driving cars"
    - "end to end learning of driving models from large scale video datasets"
    - "deepdriving learning affordance for direct perception in autonomous driving"

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.CV(Computer Vision and Pattern Recognition)