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 "cellular traffic prediction and classification a comparative evaluation of lstm and arima"):
  Abstract: Prediction of user traffic in cellular networks has attracted profound attention for improving resource utilization. In this paper, we study the problem of network traffic traffic prediction and classification by employing standard machine learning and statistical learning time series prediction methods, including long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA), respectively. We present an extensive experimental evaluation of the designed tools over a real network traffic dataset. Within this analysis, we explore the impact of different parameters to the effectiveness of the predictions. We further extend our analysis to the problem of network traffic classification and prediction of traffic bursts. The results, on the one hand, demonstrate superior performance of LSTM over ARIMA in general, especially when the length of the training time series is high enough, and it is augmented by a wisely-selected set of features. On the other hand, the results shed light on the circumstances in which, ARIMA performs close to the optimal with lower complexity.

Includes members:
  Member "a vision of 6g wireless systems applications trends technologies and open research problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "performance analysis of mobile cellular connected drones under practical antenna configurations")
  Member "self organized low power iot networks a distributed learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "user traffic prediction for proactive resource management learning powered approaches")
  Member "stock price correlation coefficient prediction with arima lstm hybrid model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "user traffic prediction for proactive resource management learning powered approaches")
  Member "risk aware resource allocation for urllc challenges and strategies with machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "experienced deep reinforcement learning with generative adversarial networks gans for model free ultra reliable low latency communication")
  Member "deep learning for encrypted traffic classification an overview":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a target agnostic attack on deep models exploiting security vulnerabilities of transfer learning")
  Member "drag deep reinforcement learning based base station activation in heterogeneous networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "user traffic prediction for proactive resource management learning powered approaches")
  Member "modeling long and short term temporal patterns with deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robusttad robust time series anomaly detection via decomposition and convolutional neural networks")
  Member "artificial neural networks based machine learning for wireless networks a tutorial":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fog computing systems state of the art research issues and future trends")

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.NI(Networking and Internet Architecture)