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: "learning to steer by mimicking features from heterogeneous auxiliary networks"
  Abstract: The training of many existing end-to-end steering angle prediction models heavily relies on steering angles as the supervisory signal. Without learning from much richer contexts, these methods are susceptible to the presence of sharp road curves, challenging traffic conditions, strong shadows, and severe lighting changes. In this paper, we considerably improve the accuracy and robustness of predictions through heterogeneous auxiliary networks feature mimicking, a new and effective training method that provides us with much richer contextual signals apart from steering direction. Specifically, we train our steering angle predictive model by distilling multi-layer knowledge from multiple heterogeneous auxiliary networks that perform related but different tasks, e.g., image segmentation or optical flow estimation. As opposed to multi-task learning, our method does not require expensive annotations of related tasks on the target set. This is made possible by applying contemporary off-the-shelf networks on the target set and mimicking their features in different layers after transformation. The auxiliary networks are discarded after training without affecting the runtime efficiency of our model. Our approach achieves a new state-of-the-art on Udacity and Comma.ai, outperforming the previous best by a large margin of 12.8% and 52.1%, respectively. Encouraging results are also shown on Berkeley Deep Drive (BDD) dataset.

Connected through hyperedge HE-1 (induced by source paper "hearing lips improving lip reading by distilling speech recognizers"):
  with members:
    - "a cascade sequence to sequence model for chinese mandarin lip reading"
    - "large scale visual speech recognition"
    - "audio visual speech recognition with a hybrid ctc attention architecture"
    - "smoothgrad removing noise by adding noise"
    - "attention is all you need"
    - "tacotron towards end to end speech synthesis"
    - "aishell 1 an open source mandarin speech corpus and a speech recognition baseline"
    - "combining residual networks with lstms for lipreading"

Connected through hyperedge HE-2 (induced by source paper "learning accurate comfortable and human like driving"):
  with members:
    - "object referring in videos with language and human gaze"
    - "conditional affordance learning for driving in urban environments"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "driving policy transfer via modularity and abstraction"
    - "learning to drive from simulation without real world labels"
    - "learning to drive in a day"
    - "on offline evaluation of vision based driving models"
    - "event based vision meets deep learning on steering prediction for self driving cars"

Connected through hyperedge HE-3 (induced by source paper "lates latent space distillation for teacher student driving policy learning"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "learning by cheating"
    - "mono3d monocular 3d vehicle detection with two scale 3d hypotheses and task priors"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "conditional affordance learning for driving in urban environments"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "virtual to real reinforcement learning for autonomous driving"
    - "carla an open urban driving simulator"

Connected through hyperedge HE-4 (induced by source paper "learning lightweight lane detection cnns by self attention distillation"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "towards end to end lane detection an instance segmentation approach"
    - "el gan embedding loss driven generative adversarial networks for lane detection"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "spatial as deep spatial cnn for traffic scene understanding"
    - "residual attention network for image classification"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"

Connected through hyperedge HE-5 (induced by source paper "accurate trajectory prediction for autonomous vehicles"):
  with members:
    - "learning accurate comfortable and human like driving"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "improving semantic segmentation via video propagation and label relaxation"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "end to end learning for self driving cars"
    - "end to end learning of driving models from large scale video datasets"

Connected through hyperedge HE-6 (induced by source paper "agnostic lane detection"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "towards end to end lane detection an instance segmentation approach"
    - "el gan embedding loss driven generative adversarial networks for lane detection"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "spatial as deep spatial cnn for traffic scene understanding"
    - "feature pyramid networks for object detection"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "using segmentation masks in the iccv 2019 learning to drive challenge"):
  with members:
    - "learning accurate comfortable and human like driving"
    - "improving semantic segmentation via video propagation and label relaxation"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "end to end learning for self driving cars"
    - "end to end learning of driving models from large scale video datasets"

Connected through hyperedge HE-8 (induced by source paper "winning the iccv 2019 learning to drive challenge"):
  with members:
    - "learning accurate comfortable and human like driving"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "end to end learning for self driving cars"

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)