human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "encoding candlesticks as images for patterns classification using convolutional neural networks"):
  Abstract: Candlestick charts display the high, low, open and closing prices for a specific time series. Candlestick patterns emerge because human actions and reactions are patterned and continuously replicate and captured in the formation of the candles. According to Thomas Bulkowski's Encyclopedia of Candlestick Charts, there are 103 candlestick patterns. Traders use these patterns to determine when to enter and exit. However, the candlestick patterns classification takes the hard work out of identifying them visually. In this paper, we propose an extend Convolutional Neural Networks (CNN) approach "GASF-CNN" to recognize the candlestick patterns automatically. We use the Gramian Angular Field (GAF) to encode the time series as different types of images. Then we use the CNN with the GAF encoding images to learn eight critical kinds of candlestick patterns. The simulation and experimental results evidence that our approach can find the eight types of candlestick patterns over eighty percent accuracy automatically.

Includes members:
  Member "on the origin of deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "high frequency component helps explain the generalization of convolutional neural networks")
  Member "deep learning for time series analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rethinking 1d cnn for time series classification a stronger baseline")

Question: Please tell me which class the source paper 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)