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: "an introductory study on time series modeling and forecasting"
  Abstract: Modeling and forecasting of time series data has fundamental importance in various practical domains. The aim of this book is to present a concise description of some popular time series forecasting models with their salient features. Three important classes of time series models, viz. stochastic, neural networks and support vector machines are studied together with their inherent forecasting strengths and weaknesses. The book also meticulously discusses about several basic issues related to time series analysis, such as stationarity, parsimony, overfitting, etc. Our study is enriched by presenting the empirical forecasting results, conducted on six real-world time series datasets. Five performance measures are used to evaluate the forecasting accuracies of different models as well as to compare the models. For each of the six time series datasets, we further show the obtained forecast diagram which graphically depicts the closeness between the original and predicted observations.

Connected through hyperedge HE-1 (induced by source paper "towards better forecasting by fusing near and distant future visions"):
  with members:
    - "correlated time series forecasting using deep neural networks a summary of results"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "autoregressive convolutional neural networks for asynchronous time series"
    - "an overview of multi task learning in deep neural networks"
    - "towards dropout training for convolutional neural networks"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (induced by source paper "time series featurization via topological data analysis"):
  with members:
    - "deep learning for time series classification a review"
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "a short survey of topological data analysis in time series and systems analysis"
    - "quasi periodicity quantification in video data using topology"
    - "persistent homology of attractors for action recognition"
    - "introduction to the r package tda"
    - "towards persistence based reconstruction in euclidean spaces"

Connected through hyperedge HE-3 (induced by source paper "impact of data normalization on deep neural network for time series forecasting"):
  with members:
    - "deep learning in neural networks"

Connected through hyperedge HE-4 (induced by source paper "augury a time series based application for the analysis and forecasting of system and network performance metrics"):
  with members:
    - "a review of network traffic analysis and prediction techniques"

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)