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: "neutm a neural network based framework for traffic matrix prediction in sdn"
  Abstract: This paper presents NeuTM, a framework for network Traffic Matrix (TM) prediction based on Long Short-Term Memory Recurrent Neural Networks (LSTM RNNs). TM prediction is defined as the problem of estimating future network traffic matrix from the previous and achieved network traffic data. It is widely used in network planning, resource management and network security. Long Short-Term Memory (LSTM) is a specific recurrent neural network (RNN) architecture that is well-suited to learn from data and classify or predict time series with time lags of unknown size. LSTMs have been shown to model long-range dependencies more accurately than conventional RNNs. NeuTM is a LSTM RNN-based framework for predicting TM in large networks. By validating our framework on real-world data from GEEANT network, we show that our model converges quickly and gives state of the art TM prediction performance.

Connected through hyperedge HE-1 (induced by source paper "cellular traffic prediction and classification a comparative evaluation of lstm and arima"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "self organized low power iot networks a distributed learning approach"
    - "stock price correlation coefficient prediction with arima lstm hybrid model"
    - "risk aware resource allocation for urllc challenges and strategies with machine learning"
    - "deep learning for encrypted traffic classification an overview"
    - "drag deep reinforcement learning based base station activation in heterogeneous networks"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "artificial neural networks based machine learning for wireless networks a tutorial"

Connected through hyperedge HE-2 (induced by source paper "user traffic prediction for proactive resource management learning powered approaches"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "self organized low power iot networks a distributed learning approach"
    - "stock price correlation coefficient prediction with arima lstm hybrid model"
    - "risk aware resource allocation for urllc challenges and strategies with machine learning"
    - "drag deep reinforcement learning based base station activation in heterogeneous networks"
    - "modeling long and short term temporal patterns with deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "deep learning based caching for self driving car in multi access edge computing"):
  with members:
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "joint communication computation caching and control in big data multi access edge computing"
    - "network slicing for service oriented networks under resource constraints"
    - "dager deep age gender and emotion recognition using convolutional neural network"
    - "optimal geographic caching in cellular networks"
    - "very deep convolutional networks for large scale image recognition"

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.NI(Networking and Internet Architecture)