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: "predicting transportation modes of gps trajectories using feature engineering and noise removal"
  Abstract: Understanding transportation mode from GPS (Global Positioning System) traces is an essential topic in the data mobility domain. In this paper, a framework is proposed to predict transportation modes. This framework follows a sequence of five steps: (i) data preparation, where GPS points are grouped in trajectory samples; (ii) point features generation; (iii) trajectory features extraction; (iv) noise removal; (v) normalization. We show that the extraction of the new point features: bearing rate, the rate of rate of change of the bearing rate and the global and local trajectory features, like medians and percentiles enables many classifiers to achieve high accuracy (96.5%) and f1 (96.3%) scores. We also show that the noise removal task affects the performance of all the models tested. Finally, the empirical tests where we compare this work against state-of-art transportation mode prediction strategies show that our framework is competitive and outperforms most of them.

Connected through hyperedge HE-1 (induced by source paper "from data to actions in intelligent transportation systems a prescription of functional requirements for model actionability"):
  with members:
    - "generalizing from a few examples a survey on few shot learning"
    - "explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai"
    - "explanation methods in deep learning users values concerns and challenges"
    - "transferable pedestrian motion prediction models at intersections"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "understanding traffic density from large scale web camera data"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "when traffic flow prediction meets wireless big data analytics"

Connected through hyperedge HE-2 (induced by source paper "transportation modes classification using feature engineering"):
  with members:
    - "inferring transportation modes from gps trajectories using a convolutional neural network"
    - "predicting crime using spatial features"
    - "feature selection a data perspective"
    - "spatio temporal data mining a survey of problems and methods"
    - "trajectorynet an embedded gps trajectory representation for point based classification using recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "on feature selection and evaluation of transportation mode prediction strategies"):
  with members:
    - "inferring transportation modes from gps trajectories using a convolutional neural network"
    - "feature selection a data perspective"
    - "spatio temporal data mining a survey of problems and methods"
    - "scikit learn machine learning in python"

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.OH(Other Computer Science)