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: "a long short term memory recurrent neural network framework for network traffic matrix prediction"
  Abstract: Network Traffic Matrix (TM) prediction is defined as the problem of estimating future network traffic 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 experience to classify, process and predict time series with time lags of unknown size. LSTMs have been shown to model temporal sequences and their long-range dependencies more accurately than conventional RNNs. In this paper, we propose a LSTM RNN framework for predicting short and long term Traffic Matrix (TM) in large networks. By validating our framework on real-world data from GEANT network, we show that our LSTM models converge quickly and give state of the art TM prediction performance for relatively small sized models.

Connected through hyperedge HE-1 (induced by source paper "contextualized spatial temporal network for taxi origin destination demand prediction"):
  with members:
    - "crowd counting using deep recurrent spatial aware network"
    - "contrast oriented deep neural networks for salient object detection"
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "facial landmark machines a backbone branches architecture with progressive representation learning"
    - "non local neural networks"
    - "short term forecasting of passenger demand under on demand ride services a spatio temporal deep learning approach"
    - "disc deep image saliency computing via progressive representation learning"
    - "lstm cf unifying context modeling and fusion with lstms for rgb d scene labeling"

Connected through hyperedge HE-2 (induced by source paper "which channel to ask my question personalized customer service requeststream routing using deepreinforcement learning"):
  with members:
    - "the mirage of action dependent baselines in reinforcement learning"
    - "deep reinforcement learning for join order enumeration"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "proximal policy optimization algorithms"
    - "a machine learning framework for resource allocation assisted by cloud computing"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-3 (induced by source paper "neuroute predictive dynamic routing for software defined networks"):
  with members:
    - "adam a method for stochastic optimization"
    - "fast emergency paths schema to overcome transient link failures in ospf routing"
    - "faster approximation schemes for fractional multicommodity flow problems via dynamic graph algorithms"

Connected through hyperedge HE-4 (induced by source paper "temporal anomaly detection calibrating the surprise"):
  with members:
    - "pu learning for matrix completion"
    - "robust principal component analysis"

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)