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: "automatic construction of a recurrent neural network based classifier for vehicle passage detection"
  Abstract: Recurrent Neural Networks (RNNs) are extensively used for time-series modeling and prediction. We propose an approach for automatic construction of a binary classifier based on Long Short-Term Memory RNNs (LSTM-RNNs) for detection of a vehicle passage through a checkpoint. As an input to the classifier we use multidimensional signals of various sensors that are installed on the checkpoint. Obtained results demonstrate that the previous approach to handcrafting a classifier, consisting of a set of deterministic rules, can be successfully replaced by an automatic RNN training on an appropriately labelled data.

Connected through hyperedge HE-1 (induced by source paper "procedural synthesis of remote sensing images for robust change detection with neural networks"):
  with members:
    - "urban change detection for multispectral earth observation using convolutional neural networks"
    - "targeted change detection in remote sensing images"
    - "fully convolutional network for automatic road extraction from satellite imagery"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "focal loss for dense object detection"
    - "carla an open urban driving simulator"

Connected through hyperedge HE-2 (induced by source paper "rare failure prediction via event matching for aerospace applications"):
  with members:
    - "topological data analysis of time series data for b2b customer relationship management"
    - "demand forecasting techniques for build to order lean manufacturing supply chains"
    - "learning ensembles of anomaly detectors on synthetic data"
    - "adaptive hedging under delayed feedback"
    - "aggregating strategies for long term forecasting"
    - "anonymous walk embeddings"
    - "machine learning in appearance based robot self localization"
    - "gtapprox surrogate modeling for industrial design"

Connected through hyperedge HE-3 (induced by source paper "esports pro players behavior during the game events statistical analysis of data obtained using the smart chair"):
  with members:
    - "understanding cyber athletes behaviour through a smart chair cs go and monolith team scenario"

Connected through hyperedge HE-4 (induced by source paper "data driven model for the identification of the rock type at a drilling bit"):
  with members:
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "xgboost a scalable tree boosting system"

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)