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: "robuststl a robust seasonal trend decomposition algorithm for long time series"
  Abstract: Decomposing complex time series into trend, seasonality, and remainder components is an important task to facilitate time series anomaly detection and forecasting. Although numerous methods have been proposed, there are still many time series characteristics exhibiting in real-world data which are not addressed properly, including 1) ability to handle seasonality fluctuation and shift, and abrupt change in trend and reminder; 2) robustness on data with anomalies; 3) applicability on time series with long seasonality period. In the paper, we propose a novel and generic time series decomposition algorithm to address these challenges. Specifically, we extract the trend component robustly by solving a regression problem using the least absolute deviations loss with sparse regularization. Based on the extracted trend, we apply the the non-local seasonal filtering to extract the seasonality component. This process is repeated until accurate decomposition is obtained. Experiments on different synthetic and real-world time series datasets demonstrate that our method outperforms existing solutions.

Connected through hyperedge HE-1 (induced by source paper "robusttad robust time series anomaly detection via decomposition and convolutional neural networks"):
  with members:
    - "robustperiod time frequency mining for robust multiple periodicities detection"
    - "time series anomaly detection service at microsoft"
    - "deep semantic segmentation of natural and medical images a review"
    - "time series anomaly detection using convolutional neural networks and transfer learning"
    - "deeply supervised salient object detection with short connections"
    - "robusttrend a huber loss with a combined first and second order difference regularization for time series trend filtering"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data"

Connected through hyperedge HE-2 (induced by source paper "time series data augmentation for deep learning a survey"):
  with members:
    - "robusttad robust time series anomaly detection via decomposition and convolutional neural networks"
    - "robustperiod time frequency mining for robust multiple periodicities detection"
    - "augmenting physiological time series data a case study for sleep apnea detection"
    - "time series anomaly detection using convolutional neural networks and transfer learning"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "deep learning for anomaly detection a survey"
    - "learning data manipulation for augmentation and weighting"
    - "robusttrend a huber loss with a combined first and second order difference regularization for time series trend filtering"

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)