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: "tensor representation in high frequency financial data for price change prediction"
  Abstract: Nowadays, with the availability of massive amount of trade data collected, the dynamics of the financial markets pose both a challenge and an opportunity for high frequency traders. In order to take advantage of the rapid, subtle movement of assets in High Frequency Trading (HFT), an automatic algorithm to analyze and detect patterns of price change based on transaction records must be available. The multichannel, time-series representation of financial data naturally suggests tensor-based learning algorithms. In this work, we investigate the effectiveness of two multilinear methods for the mid-price prediction problem against other existing methods. The experiments in a large scale dataset which contains more than 4 millions limit orders show that by utilizing tensor representation, multilinear models outperform vector-based approaches and other competing ones.

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"
    - "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"
    - "multilinear class specific discriminant analysis"
    - "cryptodl deep neural networks over encrypted data"
    - "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 "financial time series forecasting with deep learning a systematic literature review 2005 2019"):
  with members:
    - "financial trading model with stock bar chart image time series with deep convolutional neural networks"
    - "forecasting economics and financial time series arima vs lstm"
    - "sentiment predictability for stocks"
    - "lstm a search space odyssey"
    - "relational autoencoder for feature extraction"
    - "listening to chaotic whispers a deep learning framework for news oriented stock trend prediction"
    - "decision support from financial disclosures with deep neural networks and transfer learning"
    - "searching for activation functions"

Connected through hyperedge HE-4 (induced by source paper "adversarial recovery of agent rewards from latent spaces of the limit order book"):
  with members:
    - "towards inverse reinforcement learning for limit order book dynamics"
    - "model based reinforcement learning for atari"
    - "world models"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "reinforcement learning with deep energy based policies"
    - "a neural representation of sketch drawings"
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-5 (induced by source paper "a joint autoencoder for prediction and its application in gps trajectory data"):
  with members:
    - "lstm a search space odyssey"
    - "improving palliative care with deep learning"
    - "convolutional recurrent neural networks for music classification"
    - "predicting alzheimer s disease a neuroimaging study with 3d convolutional neural networks"
    - "pcanet a simple deep learning baseline for image classification"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "a convolutional neural network for modelling sentences"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-6 (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"
    - "multilinear class specific discriminant analysis"
    - "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-7 (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"
    - "benchmark dataset for mid price prediction of limit order book data"
    - "learning bag of features pooling for deep convolutional neural networks"
    - "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-8 (induced by source paper "improving efficiency in convolutional neural networks with multilinear filters"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "balanced quantization an effective and efficient approach to quantized neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "tensor contraction layers for parsimonious deep nets"
    - "learning structured sparsity in deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "hardware oriented approximation of convolutional neural networks"
    - "quantized neural networks training neural networks with low precision weights and activations"

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)