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: "an artificial neural network based stock trading system using technical analysis and big data framework"
  Abstract: In this paper, a neural network-based stock price prediction and trading system using technical analysis indicators is presented. The model developed first converts the financial time series data into a series of buy-sell-hold trigger signals using the most commonly preferred technical analysis indicators. Then, a Multilayer Perceptron (MLP) artificial neural network (ANN) model is trained in the learning stage on the daily stock prices between 1997 and 2007 for all of the Dow30 stocks. Apache Spark big data framework is used in the training stage. The trained model is then tested with data from 2007 to 2017. The results indicate that by choosing the most appropriate technical indicators, the neural network model can achieve comparable results against the Buy and Hold strategy in most of the cases. Furthermore, fine tuning the technical indicators and/or optimization strategy can enhance the overall trading performance.

Connected through hyperedge HE-1 (induced by source paper "financial series prediction using attention lstm"):
  with members:
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "attention is all you need"
    - "financial series prediction comparison between precision of time series models and machine learning methods"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "convolutional neural networks for sentence classification"
    - "neural machine translation by jointly learning to align and translate"
    - "show and tell a neural image caption generator"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "variational quantum circuits and deep reinforcement learning"):
  with members:
    - "ns3 gym extending openai gym for networking research"
    - "asynchronous methods for deep reinforcement learning"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-3 (induced by source paper "financial trading model with stock bar chart image time series with deep convolutional neural networks"):
  with members:
    - "an analysis of deep neural network models for practical applications"
    - "a convolutional neural network for modelling sentences"

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)