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: "improving efficiency in convolutional neural networks with multilinear filters"
  Abstract: Abstract   The excellent performance of deep neural networks has enabled us to solve several automatization problems, opening an era of autonomous devices. However, current deep net architectures are heavy with millions of parameters and require billions of floating point operations. Several works have been developed to compress a pre-trained deep network to reduce memory footprint and, possibly, computation. Instead of compressing a pre-trained network, in this work, we propose a generic neural network layer structure employing multilinear projection as the primary feature extractor. The proposed architecture requires several times less memory as compared to the traditional Convolutional Neural Networks (CNN), while inherits the similar design principles of a CNN. In addition, the proposed architecture is equipped with two computation schemes that enable computation reduction or scalability. Experimental results show the effectiveness of our compact projection that outperforms traditional CNN, while requiring far fewer parameters.

Connected through hyperedge HE-1 (induced by source paper "heterogeneous multilayer generalized operational perceptron"):
  with members:
    - "layer wise training of deep networks using kernel similarity"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "progressive learning for systematic design of large neural networks"
    - "a survey of model compression and acceleration for deep neural networks"
    - "wide residual networks"
    - "neural architecture search with reinforcement learning"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "data normalization for bilinear structures in high frequency financial time series"):
  with members:
    - "multilinear compressive learning with prior knowledge"
    - "multilinear compressive learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "benchmark dataset for mid price prediction of limit order book data"
    - "multilinear class specific discriminant analysis"
    - "tensor representation in high frequency financial data for price change prediction"
    - "temporal attention augmented bilinear network for financial time series data analysis"
    - "instance normalization the missing ingredient for fast stylization"

Connected through hyperedge HE-3 (induced by source paper "multilinear compressive learning"):
  with members:
    - "compressed domain image classification using a multi rate neural network"
    - "compressively sensed image recognition"
    - "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"
    - "identity mappings in deep residual networks"

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)