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: "compressed learning a deep neural network approach"
  Abstract: Compressed Learning (CL) is a joint signal processing and machine learning framework for inference from a signal, using a small number of measurements obtained by linear projections of the signal. In this paper we present an end-to-end deep learning approach for CL, in which a network composed of fully-connected layers followed by convolutional layers perform the linear sensing and non-linear inference stages. During the training phase, the sensing matrix and the non-linear inference operator are jointly optimized, and the proposed approach outperforms state-of-the-art for the task of image classification. For example, at a sensing rate of 1% (only 8 measurements of 28 X 28 pixels images), the classification error for the MNIST handwritten digits dataset is 6.46% compared to 41.06% with state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "multilinear compressive learning with prior knowledge"):
  with members:
    - "multilinear compressive learning"
    - "reversible privacy preservation using multi level encryption and compressive sensing"
    - "compressively sensed image recognition"
    - "large scale distributed neural network training through online distillation"
    - "deep mutual learning"
    - "visual relationship detection with internal and external linguistic knowledge distillation"
    - "identity mappings in deep residual networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "multilinear compressive learning"):
  with members:
    - "compressed domain image classification using a multi rate neural network"
    - "compressively sensed image recognition"
    - "improving efficiency in convolutional neural networks with multilinear filters"
    - "tensor train recurrent neural networks for video classification"
    - "multilinear class specific discriminant analysis"
    - "cryptodl deep neural networks over encrypted data"
    - "tensor representation in high frequency financial data for price change prediction"
    - "temporal attention augmented bilinear network for financial time series data analysis"

Connected through hyperedge HE-3 (induced by source paper "compressed domain image classification using a multi rate neural network"):
  with members:
    - "rate adaptive neural networks for spatial multiplexers"
    - "reconstruction free action inference from compressive imagers"
    - "adam a method for stochastic optimization"
    - "from denoising to compressed sensing"
    - "stable image reconstruction using total variation minimization"

Connected through hyperedge HE-4 (induced by source paper "compressively sensed image recognition"):
  with members:
    - "convolutional neural networks for non iterative reconstruction of compressively sensed images"
    - "reconstruction free action inference from compressive imagers"
    - "from denoising to compressed sensing"

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)