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: "improving the performance of the lstm and hmm models via hybridization"
  Abstract: Language models based on deep neural neural networks and traditionalstochastic modelling has become both highly functional and effective in recenttimes. In this work a general survey into the two types of language modelling is conducted. We investigate the effectiveness of a combination of the Hidden Markov Model (HMM) with the Long Short-Term Memory (LSTM) model via a process known as hybridization, which we introduce in this paper. This process involves combining the substitution of hidden state probabilities of the HMM into those of the LSTM. We conduct Monte Carlo sampling to produce training and validation of the data in order to produce robust results. The experimental results of this work displayed an increase in the predictive accuracy of LSTM model when hybridized with the HMM.

Connected through hyperedge HE-1 (induced by source paper "powering hidden markov model by neural network based generative models"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "density estimation using real nvp"
    - "adam a method for stochastic optimization"
    - "nice non linear independent components estimation"

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)