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: "predicting the direction of stock market prices using random forest"
  Abstract: Predicting trends in stock market prices has been an area of interest for researchers for many years due to its complex and dynamic nature. Intrinsic volatility in stock market across the globe makes the task of prediction challenging. Forecasting and diffusion modeling, although effective can't be the panacea to the diverse range of problems encountered in prediction, short-term or otherwise. Market risk, strongly correlated with forecasting errors, needs to be minimized to ensure minimal risk in investment. The authors propose to minimize forecasting error by treating the forecasting problem as a classification problem, a popular suite of algorithms in Machine learning. In this paper, we propose a novel way to minimize the risk of investment in stock market by predicting the returns of a stock using a class of powerful machine learning algorithms known as ensemble learning. Some of the technical indicators such as Relative Strength Index (RSI), stochastic oscillator etc are used as inputs to train our model. The learning model used is an ensemble of multiple decision trees. The algorithm is shown to outperform existing algo- rithms found in the literature. Out of Bag (OOB) error estimates have been found to be encouraging. Key Words: Random Forest Classifier, stock price forecasting, Exponential smoothing, feature extraction, OOB error and convergence.

Connected through hyperedge HE-1 (induced by source paper "event ticket price prediction with deep neural network on spatial temporal sparse data"):
  with members:
    - "revisiting spatial temporal similarity a deep learning framework for traffic prediction"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "short term forecasting of passenger demand under on demand ride services a spatio temporal deep learning approach"
    - "spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks"
    - "machine learning modeling for time series problem predicting flight ticket prices"
    - "xgboost a scalable tree boosting system"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"

Connected through hyperedge HE-2 (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"
    - "tensor representation in high frequency financial data for price change prediction"
    - "progressive learning for systematic design of large neural networks"
    - "neural architecture search with reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "u cnnpred a universal cnn based predictor for stock markets"):
  with members:
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-4 (induced by source paper "cnnpred cnn based stock market prediction using several data sources"):
  with members:
    - "deep residual learning for image recognition"
    - "bayesian regression and bitcoin"
    - "improving neural networks by preventing co adaptation of feature detectors"

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.LG(Machine Learning)