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: "stock price correlation coefficient prediction with arima lstm hybrid model"
  Abstract: Predicting the price correlation of two assets for future time periods is important in portfolio optimization. We apply LSTM recurrent neural networks (RNN) in predicting the stock price correlation coefficient of two individual stocks. RNNs are competent in understanding temporal dependencies. The use of LSTM cells further enhances its long term predictive properties. To encompass both linearity and nonlinearity in the model, we adopt the ARIMA model as well. The ARIMA model filters linear tendencies in the data and passes on the residual value to the LSTM model. The ARIMA LSTM hybrid model is tested against other traditional predictive financial models such as the full historical model, constant correlation model, single index model and the multi group model. In our empirical study, the predictive ability of the ARIMA-LSTM model turned out superior to all other financial models by a significant scale. Our work implies that it is worth considering the ARIMA LSTM model to forecast correlation coefficient for portfolio optimization.

Connected through hyperedge HE-1 (induced by source paper "cellular traffic prediction and classification a comparative evaluation of lstm and arima"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "self organized low power iot networks a distributed learning approach"
    - "risk aware resource allocation for urllc challenges and strategies with machine learning"
    - "deep learning for encrypted traffic classification an overview"
    - "drag deep reinforcement learning based base station activation in heterogeneous networks"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "neutm a neural network based framework for traffic matrix prediction in sdn"

Connected through hyperedge HE-2 (induced by source paper "user traffic prediction for proactive resource management learning powered approaches"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "self organized low power iot networks a distributed learning approach"
    - "risk aware resource allocation for urllc challenges and strategies with machine learning"
    - "drag deep reinforcement learning based base station activation in heterogeneous networks"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "neutm a neural network based framework for traffic matrix prediction in sdn"

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)