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: "leverage financial news to predict stock price movements using word embeddings and deep neural networks"
  Abstract: Financial news contains useful information on public companies and the market. In this paper we apply the popular word embedding methods and deep neural networks to leverage financial news to predict stock price movements in the market. Experimental results have shown that our proposed methods are simple but very effective, which can significantly improve the stock prediction accuracy on a standard financial database over the baseline system using only the historical price information.

Connected through hyperedge HE-1 (induced by source paper "unsupervised extraction of market moving events with neural attention"):
  with members:
    - "attention is not not explanation"
    - "is attention interpretable"
    - "attention is not explanation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (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-3 (induced by source paper "modeling financial analysts decision making via the pragmatics and semantics of earnings calls"):
  with members:
    - "attentive interaction model modeling changes in view in argumentation"
    - "asking too much the rhetorical role of questions in political discourse"
    - "argument strength is in the eye of the beholder audience effects in persuasion"
    - "relnet end to end modeling of entities relations"
    - "volatility prediction using financial disclosures sentiments with word embedding based ir models"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"

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"
    - "temporal attention augmented bilinear network for financial time series data analysis"
    - "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"
    - "modelling visualising and summarising documents with a single convolutional neural network"

Connected through hyperedge HE-5 (induced by source paper "lancaster a at semeval 2017 task 5 evaluation metrics matter predicting sentiment from financial news headlines"):
  with members:
    - "a hierarchical model of reviews for aspect based sentiment analysis"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "efficient estimation of word representations in vector space"
    - "scikit learn machine learning in python"
    - "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews"

Connected through hyperedge HE-6 (induced by source paper "improving stock market prediction via heterogeneous information fusion"):
  with members:
    - "coupled matrix factorization within non iid context"
    - "distributed representations of words and phrases and their compositionality"
    - "twitter mood predicts the stock market"
    - "predicting abnormal returns from news using text classification"

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)