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: "driver identification using automobile sensor data from a single turn"
  Abstract: As automotive electronics continue to advance, cars are becoming more and more reliant on sensors to perform everyday driving operations. These sensors are omnipresent and help the car navigate, reduce accidents, and provide comfortable rides. However, they can also be used to learn about the drivers themselves. In this paper, we propose a method to predict, from sensor data collected at a single turn, the identity of a driver out of a given set of individuals. We cast the problem in terms of time series classification, where our dataset contains sensor readings at one turn, repeated several times by multiple drivers. We build a classifier to find unique patterns in each individual's driving style, which are visible in the data even on such a short road segment. To test our approach, we analyze a new dataset collected by AUDI AG and Audi Electronics Venture, where a fleet of test vehicles was equipped with automotive data loggers storing all sensor readings on real roads. We show that turns are particularly well-suited for detecting variations across drivers, especially when compared to straightaways. We then focus on the 12 most frequently made turns in the dataset, which include rural, urban, highway on-ramps, and more, obtaining accurate identification results and learning useful insights about driver behavior in a variety of settings.

Connected through hyperedge HE-1 (induced by source paper "driver identification by neural network on extracted statistical features from smartphone data"):
  with members:
    - "regularized deep networks in intelligent transportation systems a taxonomy and a case study"
    - "smartphone based driving style classification using features made by discrete wavelet transform"
    - "smartphone based vehicle telematics a ten year anniversary"

Connected through hyperedge HE-2 (induced by source paper "automatic driver identification from in vehicle network logs"):
  with members:
    - "deep learning for time series classification a review"
    - "extracting vehicle sensor signals from can logs for driver re identification"
    - "are efficient deep representations learnable"
    - "driving behavior analysis through can bus data in an uncontrolled environment"
    - "deepsense a unified deep learning framework for time series mobile sensing data processing"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "multi scale convolutional neural networks for time series classification"
    - "end to end attention based large vocabulary speech recognition"

Connected through hyperedge HE-3 (induced by source paper "driver identification based on vehicle telematics data using lstm recurrent neural network"):
  with members:
    - "know your master driver profiling based anti theft method"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "noise tolerance under risk minimization"

Connected through hyperedge HE-4 (induced by source paper "extracting vehicle sensor signals from can logs for driver re identification"):
  with members:
    - "driving behavior analysis through can bus data in an uncontrolled environment"
    - "the great time series classification bake off an experimental evaluation of recently proposed algorithms extended version"

Connected through hyperedge HE-5 (induced by source paper "drive2vec multiscale state space embedding of vehicular sensor data"):
  with members:
    - "toeplitz inverse covariance based clustering of multivariate time series data"
    - "node2vec scalable feature learning for networks"
    - "temporal convolutional networks a unified approach to action segmentation"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "how to construct deep recurrent neural networks"
    - "efficient estimation of word representations in vector space"

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.HC(Human-Computer Interaction)