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: "training a network to attend like human drivers saves it from common but misleading loss functions"
  Abstract: We proposed a novel FCN-ConvLSTM model to predict multi-focal human driver's#N#attention merely from monocular dash camera videos. Our model has surpassed the#N#state-of-the-art performance and demonstrated sophisticated behaviors such as#N#watching out for a driver exiting from a parked car. In addition, we have#N#demonstrated a surprising paradox: fine-tuning AlexNet on a largescale driving#N#dataset degraded its ability to register pedestrians. This is due to the fact#N#that the commonly practiced training paradigm has failed to reflect the#N#different importance levels of the frames of the driving video datasets. As a#N#solution, we propose to unequally sample the learning frames at appropriate#N#probabilities and introduced a way of using human gaze to determine the#N#sampling weights. We demonstrated the effectiveness of this proposal in human#N#driver attention prediction, which we believe can also be generalized to other#N#driving-related machine learning tasks.

Connected through hyperedge HE-1 (induced by source paper "goal oriented object importance estimation in on road driving videos"):
  with members:
    - "startnet online detection of action start in untrimmed videos"
    - "learning rich features for image manipulation detection"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "temporal recurrent networks for online action detection"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "predicting driver attention in critical situations"
    - "an analysis of scale invariance in object detection snip"
    - "dynamic zoom in network for fast object detection in large images"

Connected through hyperedge HE-2 (induced by source paper "risk aware reasoning for autonomous vehicles"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "predicting driver attention in critical situations"
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "the social dilemma of autonomous vehicles"
    - "shallow and deep convolutional networks for saliency prediction"
    - "instance level segmentation for autonomous driving with deep densely connected mrfs"
    - "decentralized control of partially observable markov decision processes using belief space macro actions"

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)