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 "user traffic prediction for proactive resource management learning powered approaches"):
  Abstract: Traffic prediction plays a vital role in efficient planning and usage of network resources in wireless networks. While traffic prediction in wired networks is an established field, there is a lack of research on the analysis of traffic in cellular networks, especially in a content-blind manner at the user level. Here, we shed light into this problem by designing traffic prediction tools that employ either statistical, rule-based, or deep machine learning methods. First, we present an extensive experimental evaluation of the designed tools over a real traffic dataset. Within this analysis, the impact of different parameters, such as length of prediction, feature set used in analyses, and granularity of data, on accuracy of prediction are investigated. Second, regarding the coupling observed between behavior of traffic and its generating application, we extend our analysis to the blind classification of applications generating the traffic based on the statistics of traffic arrival/departure. The results demonstrate presence of a threshold number of previous observations, beyond which, deep machine learning can outperform linear statistical learning, and before which, statistical learning outperforms deep learning approaches. Further analysis of this threshold value represents a strong coupling between this threshold, the length of future prediction, and the feature set in use. Finally, through a case study, we present how the experienced delay could be decreased by traffic arrival prediction.

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 "cellular traffic prediction and classification a comparative evaluation of lstm and arima")
  Member "stock price correlation coefficient prediction with arima lstm hybrid model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cellular traffic prediction and classification a comparative evaluation of lstm and arima")
  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 "drag deep reinforcement learning based base station activation in heterogeneous networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cellular traffic prediction and classification a comparative evaluation of lstm and arima")
  Member "modeling long and short term temporal patterns with deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robusttad robust time series anomaly detection via decomposition and convolutional neural networks")
  Member "neutm a neural network based framework for traffic matrix prediction in sdn":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cellular traffic prediction and classification a comparative evaluation of lstm and arima")

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)