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 survey time travel in deep learning space an introduction to deep learning models and how deep learning models evolved from the initial ideas"
  Abstract: This report will show the history of deep learning evolves. It will trace back as far as the initial belief of connectionism modelling of brain, and come back to look at its early stage realization: neural networks. With the background of neural network, we will gradually introduce how convolutional neural network, as a representative of deep discriminative models, is developed from neural networks, together with many practical techniques that can help in optimization of neural networks. On the other hand, we will also trace back to see the evolution history of deep generative models, to see how researchers balance the representation power and computation complexity to reach Restricted Boltzmann Machine and eventually reach Deep Belief Nets. Further, we will also look into the development history of modelling time series data with neural networks. We start with Time Delay Neural Networks and move further to currently famous model named Recurrent Neural Network and its extension Long Short Term Memory. We will also briefly look into how to construct deep recurrent neural networks. Finally, we will conclude this report with some interesting open-ended questions of deep neural networks.

Connected through hyperedge HE-1 (induced by source paper "on architectural choices in deep learning from network structure to gradient convergence and parameter estimation"):
  with members:
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "learning to learn by gradient descent by gradient descent"
    - "why are deep nets reversible a simple theory with implications for training"
    - "learning transferable features with deep adaptation networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "train faster generalize better stability of stochastic gradient descent"
    - "fitnets hints for thin deep nets"
    - "going deeper with convolutions"

Connected through hyperedge HE-2 (induced by source paper "on the interplay of network structure and gradient convergence in deep learning"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "why are deep nets reversible a simple theory with implications for training"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "train faster generalize better stability of stochastic gradient descent"
    - "constrained convolutional neural networks for weakly supervised segmentation"
    - "how transferable are features in deep neural networks"
    - "fitnets hints for thin deep nets"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "multimodal transfer deep learning with applications in audio visual recognition"):
  with members:
    - "deep learning multi view representation for face recognition"
    - "mining associated text and images with dual wing harmoniums"

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)