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 accurate comfortable and human like driving"
  Abstract: Autonomous vehicles are more likely to be accepted if they drive accurately, comfortably, but also similar to how human drivers would. This is especially true when autonomous and human-driven vehicles need to share the same road. The main research focus thus far, however, is still on improving driving accuracy only. This paper formalizes the three concerns with the aim of accurate, comfortable and human-like driving. Three contributions are made in this paper. First, numerical map data from HERE Technologies are employed for more accurate driving; a set of map features which are believed to be relevant to driving are engineered to navigate better. Second, the learning procedure is improved from a pointwise prediction to a sequence-based prediction and passengers' comfort measures are embedded into the learning algorithm. Finally, we take advantage of the advances in adversary learning to learn human-like driving; specifically, the standard L1 or L2 loss is augmented by an adversary loss which is based on a discriminator trained to distinguish between human driving and machine driving. Our model is trained and evaluated on the Drive360 dataset, which features 60 hours and 3000 km of real-world driving data. Extensive experiments show that our driving model is more accurate, more comfortable and behaves more like a human driver than previous methods. The resources of this work will be released on the project page.

Connected through hyperedge HE-1 (induced by source paper "talk2nav long range vision and language navigation with dual attention and spatial memory"):
  with members:
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "learning to follow directions in street view"
    - "grounding human to vehicle advice for self driving vehicles"
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "the regretful agent heuristic aided navigation through progress estimation"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"

Connected through hyperedge HE-2 (induced by source paper "real time 3d traffic cone detection for autonomous driving"):
  with members:
    - "semantic foggy scene understanding with synthetic data"
    - "simultaneous traffic sign detection and boundary estimation using convolutional neural network"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "perceptual generative adversarial networks for small object detection"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-3 (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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "improved reinforcement learning through imitation learning pretraining towards image based autonomous driving"):
  with members:
    - "drone path following in gps denied environments using convolutional networks"
    - "bam bottleneck attention module"
    - "learning transferable uav for forest visual perception"
    - "cbam convolutional block attention module"
    - "towards monocular vision based obstacle avoidance through deep reinforcement learning"
    - "squeeze and excitation networks"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "cad2rl real single image flight without a single real image"

Connected through hyperedge HE-5 (induced by source paper "accurate trajectory prediction for autonomous vehicles"):
  with members:
    - "learning to steer by mimicking features from heterogeneous auxiliary networks"
    - "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 "using segmentation masks in the iccv 2019 learning to drive challenge"):
  with members:
    - "learning to steer by mimicking features from heterogeneous auxiliary networks"
    - "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-7 (induced by source paper "winning the iccv 2019 learning to drive challenge"):
  with members:
    - "learning to steer by mimicking features from heterogeneous auxiliary networks"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "learning a curve guardian for motorcycles"):
  with members:
    - "end to end learning of driving models with surround view cameras and route planners"
    - "real time control for autonomous racing based on viability theory"
    - "deep residual learning for image recognition"
    - "julia a fresh approach to numerical computing"

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)