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: "training input output recurrent neural networks through spectral methods"
  Abstract: Author(s): Sedghi, Hanie; Anandkumar, Anima | Abstract: We consider the problem of training input-output recurrent neural networks (RNN) for sequence labeling tasks. We propose a novel spectral approach for learning the network parameters. It is based on decomposition of the cross-moment tensor between the output and a non-linear transformation of the input, based on score functions. We guarantee consistent learning with polynomial sample and computational complexity under transparent conditions such as non-degeneracy of model parameters, polynomial activations for the neurons, and a Markovian evolution of the input sequence. We also extend our results to Bidirectional RNN which uses both previous and future information to output the label at each time point, and is employed in many NLP tasks such as POS tagging.

Connected through hyperedge HE-1 (induced by source paper "stable recurrent models"):
  with members:
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "stochastic gradient descent learns state equations with nonlinear activations"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "kronecker recurrent units"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-2 (induced by source paper "on the optimization of deep networks implicit acceleration by overparameterization"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "analysis and design of convolutional networks via hierarchical tensor decompositions"
    - "depth separation for neural networks"
    - "on the ability of neural nets to express distributions"
    - "spurious local minima are common in two layer relu neural networks"
    - "identity matters in deep learning"
    - "the power of depth for feedforward neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "connecting weighted automata and recurrent neural networks through spectral learning"):
  with members:
    - "rational recurrences"
    - "explaining black boxes on sequential data using weighted automata"
    - "expressive power of recurrent neural networks"
    - "tensor train recurrent neural networks for video classification"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "compressing recurrent neural network with tensor train"
    - "subregular complexity and deep learning"
    - "long term forecasting using higher order tensor rnns"

Connected through hyperedge HE-4 (induced by source paper "tensor regression networks"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "tensor contraction layers for parsimonious deep nets"
    - "deep multi task representation learning a tensor factorisation approach"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "tensor contractions with extended blas kernels on cpu and gpu"
    - "tensorizing neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "boosting dilated convolutional networks with mixed tensor decompositions"):
  with members:
    - "tensorial mixture models"
    - "deep networks with stochastic depth"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "densely connected convolutional networks"
    - "learning functions when is deep better than shallow"
    - "neural machine translation in linear time"
    - "the power of depth for feedforward neural networks"
    - "representation benefits of deep feedforward networks"

Connected through hyperedge HE-6 (induced by source paper "tensor contraction layers for parsimonious deep nets"):
  with members:
    - "deep multi task representation learning a tensor factorisation approach"
    - "tensor contractions with extended blas kernels on cpu and gpu"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "tensorizing neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "global optimality in tensor factorization deep learning and beyond"
    - "compression of deep convolutional neural networks for fast and low power mobile applications"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

Connected through hyperedge HE-7 (induced by source paper "deep learning and quantum entanglement fundamental connections with implications to network design"):
  with members:
    - "on the expressive power of overlapping architectures of deep learning"
    - "tensorial mixture models"
    - "learning convolutional neural networks for graphs"
    - "deep convolutional networks on graph structured data"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "spectral networks and locally connected networks on graphs"

Connected through hyperedge HE-8 (induced by source paper "prediction with a short memory"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "sum of squares lower bounds for refuting any csp"
    - "lower bounds for csp refutation by sdp hierarchies"
    - "wavenet a generative model for raw audio"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "how to refute a random csp"
    - "character aware neural language models"
    - "complexity theoretic limitations on learning halfspaces"

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)