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 "cost sensitive convolution based neural networks for imbalanced time series classification"):
  Abstract: Some deep convolutional neural networks were proposed for time-series classification and class imbalanced problems. However, those models performed degraded and even failed to recognize the minority class of an imbalanced temporal sequences dataset. Minority samples would bring troubles for temporal deep learning classifiers due to the equal treatments of majority and minority class. Until recently, there were few works applying deep learning on imbalanced time-series classification (ITSC) tasks. Here, this paper aimed at tackling ITSC problems with deep learning. An adaptive cost-sensitive learning strategy was proposed to modify temporal deep learning models. Through the proposed strategy, classifiers could automatically assign misclassification penalties to each class. In the experimental section, the proposed method was utilized to modify five neural networks. They were evaluated on a large volume, real-life and imbalanced time-series dataset with six metrics. Each single network was also tested alone and combined with several mainstream data samplers. Experimental results illustrated that the proposed cost-sensitive modified networks worked well on ITSC tasks. Compared to other methods, the cost-sensitive convolution neural network and residual network won out in the terms of all metrics. Consequently, the proposed cost-sensitive learning strategy can be used to modify deep learning classifiers from cost-insensitive to cost-sensitive. Those cost-sensitive convolutional networks can be effectively applied to address ITSC issues.

Includes members:
  Member "lstm fully convolutional networks for time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "convolutional neural networks with intermediate loss for 3d super resolution of ct and mri scans")
  Member "a systematic study of the class imbalance problem in convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "identifying and compensating for feature deviation in imbalanced deep learning")
  Member "deep learning for time series analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rethinking 1d cnn for time series classification a stronger baseline")
  Member "time series classification from scratch with deep neural networks a strong baseline":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "socialgrid a tcn enhanced method for online discussion forecasting")
  Member "multi scale convolutional neural networks for time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "socialgrid a tcn enhanced method for online discussion forecasting")
  Member "learning robust features using deep learning for automatic seizure detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "temporal graph convolutional networks for automatic seizure detection")
  Member "earliness aware deep convolutional networks for early time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep learning for time series classification a review")
  Member "cost sensitive learning of deep feature representations from imbalanced data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning from multiple experts self paced knowledge distillation for long tailed classification")

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.LG(Machine Learning)