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: "benchmark dataset for mid price prediction of limit order book data"
  Abstract: Presently, managing prediction of metrics in high frequency financial markets is a challenging task. An efficient way to do it is by monitoring the dynamics of a limit order book and try to identify the information edge. This paper describes a new benchmark dataset of high-frequency limit order markets for mid-price prediction. We make publicly available normalized representations of high frequency data for five stocks extracted from the NASDAQ Nordic stock market. Furthermore, we define an experimental protocol that can be used in order to evaluate the performance of related research methods. Baseline results based on linear and nonlinear regression models are also provided and show the potential that these methods have for mid-price prediction.

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"
    - "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-2 (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"
    - "multilinear class specific discriminant analysis"
    - "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-3 (induced by source paper "temporal logistic neural bag of features for financial time series forecasting leveraging limit order book data"):
  with members:
    - "machine learning for forecasting mid price movement using limit order book data"
    - "learning bag of features pooling for deep convolutional neural networks"
    - "tensor representation in high frequency financial data for price change prediction"
    - "multi scale convolutional neural networks for time series classification"
    - "learning to diagnose with lstm recurrent neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "using deep learning for price prediction by exploiting stationary limit order book features"):
  with members:
    - "temporal attention augmented bilinear network for financial time series data analysis"
    - "wavenet a generative model for raw audio"
    - "visual7w grounded question answering in images"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "deep captioning with multimodal recurrent neural networks m rnn"
    - "speech recognition with deep recurrent neural networks"

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"
    - "multilinear class specific discriminant analysis"
    - "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.CE(Computational Engineering, Finance, and Science)