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: "what can be predicted from six seconds of driver glances"
  Abstract: We consider a large dataset of real-world, on-road driving from a 100-car naturalistic study to explore the predictive power of driver glances and, specifically, to answer the following question: what can be predicted about the state of the driver and the state of the driving environment from a 6-second sequence of macro-glances? The context-based nature of such glances allows for application of supervised learning to the problem of vision-based gaze estimation, making it robust, accurate, and reliable in messy, real-world conditions. So, it's valuable to ask whether such macro-glances can be used to infer behavioral, environmental, and demographic variables? We analyze 27 binary classification problems based on these variables. The takeaway is that glance can be used as part of a multi-sensor real-time system to predict radio-tuning, fatigue state, failure to signal, talking, and several environment variables.

Connected through hyperedge HE-1 (induced by source paper "mit advanced vehicle technology study large scale naturalistic driving study of driver behavior and interaction with automation"):
  with members:
    - "deformable convolutional networks"
    - "understanding convolution for semantic segmentation"
    - "automated synchronization of driving data using vibration and steering events"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "end to end learning for self driving cars"
    - "pyramid scene parsing network"
    - "end to end learning of driving models from large scale video datasets"
    - "owl and lizard patterns of head pose and eye pose in driver gaze classification"

Connected through hyperedge HE-2 (induced by source paper "what do people see in a twenty second glimpse of bivariate vector field visualizations"):
  with members:
    - "toward a deep understanding of what makes a scientific visualization memorable"
    - "measuring the effects of scalar and spherical colormaps on ensembles of dmri tubes"
    - "at a glance pixel approximate entropy as a measure of line chart complexity"
    - "bivariate separable dimension glyphs can improve visual analysis of holistic features"
    - "what do different evaluation metrics tell us about saliency models"

Connected through hyperedge HE-3 (induced by source paper "dynamics of driver s gaze explorations in behavior modeling maneuver prediction"):
  with members:
    - "learning where to attend like a human driver"
    - "it s written all over your face full face appearance based gaze estimation"
    - "owl and lizard patterns of head pose and eye pose in driver gaze classification"
    - "car that knows before you do anticipating maneuvers via learning temporal driving models"

Connected through hyperedge HE-4 (induced by source paper "multimodal polynomial fusion for detecting driver distraction"):
  with members:
    - "multimodal machine learning a survey and taxonomy"
    - "recurrent polynomial network for dialogue state tracking"
    - "adam a method for stochastic optimization"

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)