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 "bag of recurrence patterns representation for time series classification"):
  Abstract: Time-series classification (TSC) has attracted a lot of attention in pattern recognition, because wide range of applications from different domains such as finance and health informatics deal with time-series signals. Bag-of-features (BoF) model has achieved a great success in TSC task by summarizing signals according to the frequencies of “feature words” of a data-learned dictionary. This paper proposes embedding the recurrence plots (RP), a visualization technique for analysis of dynamic systems, in the BoF model for TSC. While the traditional BoF approach extracts features from 1D signal segments, this paper uses the RP to transform time-series into 2D texture images and then applies the BoF on them. Image representation of time-series enables us to explore different visual descriptors that are not available for 1D signals and to treat TSC task as a texture recognition problem. Experimental results on the UCI time-series classification archive demonstrates a significant accuracy boost by the proposed bag of recurrence patterns, compared not only to the existing BoF models, but also to the state-of-the art algorithms.

Includes members:
  Member "classification of time series images using deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "implementation of deep neural networks to classify eeg signals using gramian angular summation field for epilepsy diagnosis")
  Member "dense bag of temporal sift words for time series classification":
    also connected through other hyperedges:
      (none)
  Member "scalable classification of repetitive time series through frequencies of local polynomials":
    also connected through other hyperedges:
      (none)
  Member "classifiers with a reject option for early time series classification":
    also connected through other hyperedges:
      (none)
  Member "bag of words representation for biomedical time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ts chief a scalable and accurate forest algorithm for time series classification")
  Member "ecoc based training of neural networks for face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "classification of time series images using deep convolutional neural networks")

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.CV(Computer Vision and Pattern Recognition)