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 "robusttad robust time series anomaly detection via decomposition and convolutional neural networks"):
  Abstract: The monitoring and management of numerous and diverse time series data at Alibaba Group calls for an effective and scalable time series anomaly detection service. In this paper, we propose RobustTAD, a Robust Time series Anomaly Detection framework by integrating robust seasonal-trend decomposition and convolutional neural network for time series data. The seasonal-trend decomposition can effectively handle complicated patterns in time series, and meanwhile significantly simplifies the architecture of the neural network, which is an encoder-decoder architecture with skip connections. This architecture can effectively capture the multi-scale information from time series, which is very useful in anomaly detection. Due to the limited labeled data in time series anomaly detection, we systematically investigate data augmentation methods in both time and frequency domains. We also introduce label-based weight and value-based weight in the loss function by utilizing the unbalanced nature of the time series anomaly detection problem. Compared with the widely used forecasting-based anomaly detection algorithms, decomposition-based algorithms, traditional statistical algorithms, as well as recent neural network based algorithms, RobustTAD performs significantly better on public benchmark datasets. It is deployed as a public online service and widely adopted in different business scenarios at Alibaba Group.

Includes members:
  Member "robustperiod time frequency mining for robust multiple periodicities detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "time series data augmentation for deep learning a survey")
  Member "time series anomaly detection service at microsoft":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sequential vae lstm for anomaly detection on time series")
  Member "deep semantic segmentation of natural and medical images a review":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "trust your model iterative label improvement and robust training by confidence based filtering and dataset partitioning")
  Member "time series anomaly detection using convolutional neural networks and transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "time series data augmentation for deep learning a survey")
  Member "deeply supervised salient object detection with short connections":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "robusttrend a huber loss with a combined first and second order difference regularization for time series trend filtering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "time series data augmentation for deep learning a survey")
  Member "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "likelihood regret an out of distribution detection score for variational auto encoder")
  Member "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "oiad one for all image anomaly detection with disentanglement learning")

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)