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: "temporal attention augmented bilinear network for financial time series data analysis"
  Abstract: Financial time-series forecasting has long been a challenging problem because of the inherently noisy and stochastic nature of the market. In the High-Frequency Trading (HFT), forecasting for trading purposes is even a more challenging task since an automated inference system is required to be both accurate and fast. In this paper, we propose a neural network layer architecture that incorporates the idea of bilinear projection as well as an attention mechanism that enables the layer to detect and focus on crucial temporal information. The resulting network is highly interpretable, given its ability to highlight the importance and contribution of each temporal instance, thus allowing further analysis on the time instances of interest. Our experiments in a large-scale Limit Order Book (LOB) dataset show that a two-hidden-layer network utilizing our proposed layer outperforms by a large margin all existing state-of-the-art results coming from much deeper architectures while requiring far fewer computations.

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"
    - "multilinear class specific discriminant analysis"
    - "tensor representation in high frequency financial data for price change prediction"
    - "instance normalization the missing ingredient for fast stylization"

Connected through hyperedge HE-2 (induced by source paper "error feedback stochastic configuration strategy on convolutional neural networks for time series forecasting"):
  with members:
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "accelerating neural architecture search using performance prediction"
    - "stochastic configuration networks fundamentals and algorithms"
    - "deep stochastic configuration networks with universal approximation property"
    - "a powerful generative model using random weights for the deep image representation"
    - "pso mismo modeling strategy for multistep ahead time series prediction"
    - "representation learning a review and new perspectives"

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"
    - "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"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-4 (induced by source paper "open domain event extraction and embedding for natural gas market prediction"):
  with members:
    - "forecasting economics and financial time series arima vs lstm"
    - "cevo comprehensive event ontology enhancing cognitive annotation"
    - "a deeper look into sarcastic tweets using deep convolutional neural networks"
    - "insight 1 at semeval 2016 task 5 deep learning for multilingual aspect based sentiment analysis"
    - "leverage financial news to predict stock price movements using word embeddings and deep neural networks"
    - "modelling visualising and summarising documents with a single convolutional neural network"

Connected through hyperedge HE-5 (induced by source paper "using deep learning for price prediction by exploiting stationary limit order book features"):
  with members:
    - "benchmark dataset for mid price prediction of limit order book data"
    - "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-6 (induced by source paper "machine learning for forecasting mid price movement using limit order book data"):
  with members:
    - "data driven neural architecture learning for financial time series forecasting"
    - "benchmark dataset for mid price forecasting of limit order book data with machine learning methods"
    - "tensor representation in high frequency financial data for price change prediction"
    - "adam a method for stochastic optimization"
    - "class imbalance problem in data mining review"

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)