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: "multilinear class specific discriminant analysis"
  Abstract: Abstract   There has been a great effort to transfer linear discriminant techniques that operate on vector data to high-order data, generally referred to as Multilinear Discriminant Analysis (MDA) techniques. Many existing works focus on maximizing the inter-class variances to intra-class variances defined on tensor data representations. However, there has not been any attempt to employ class-specific discrimination criteria for the tensor data. In this paper, we propose a multilinear subspace learning technique suitable for applications requiring class-specific tensor models. The method maximizes the discrimination of each individual class in the feature space while retains the spatial structure of the input. We evaluate the efficiency of the proposed method on two problems, i.e. facial image analysis and stock price prediction based on limit order book data.

Connected through hyperedge HE-1 (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"
    - "improving efficiency in convolutional neural networks with multilinear filters"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "benchmark dataset for mid price prediction of limit order book data"
    - "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-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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "data driven neural architecture learning for financial time series forecasting"):
  with members:
    - "heterogeneous multilayer generalized operational perceptron"
    - "progressive operational perceptron with memory"
    - "benchmark dataset for mid price prediction of limit order book data"
    - "tensor representation in high frequency financial data for price change prediction"
    - "progressive learning for systematic design of large neural networks"
    - "neural architecture search with reinforcement learning"
    - "predicting the direction of stock market prices using random forest"

Connected through hyperedge HE-4 (induced by source paper "client specific anomaly detection for face presentation attack detection"):
  with members:
    - "a survey of sparse representation algorithms and applications"
    - "going deeper with convolutions"

Connected through hyperedge HE-5 (induced by source paper "temporal attention augmented bilinear network for financial time series data analysis"):
  with members:
    - "benchmark dataset for mid price forecasting of limit order book data with machine learning methods"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "learning deep matrix representations"
    - "lstm a search space odyssey"
    - "benchmark dataset for mid price prediction of limit order book data"
    - "position based content attention for time series forecasting with sequence to sequence rnns"
    - "tensor representation in high frequency financial data for price change prediction"
    - "matrix neural 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)