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: "expressive power of recurrent neural networks"
  Abstract: Deep neural networks are surprisingly efficient at solving practical tasks, but the theory behind this phenomenon is only starting to catch up with the practice. Numerous works show that depth is the key to this efficiency. A certain class of deep convolutional networks -- namely those that correspond to the Hierarchical Tucker (HT) tensor decomposition -- has been proven to have exponentially higher expressive power than shallow networks. I.e. a shallow network of exponential width is required to realize the same score function as computed by the deep architecture. In this paper, we prove the expressive power theorem (an exponential lower bound on the width of the equivalent shallow network) for a class of recurrent neural networks -- ones that correspond to the Tensor Train (TT) decomposition. This means that even processing an image patch by patch with an RNN can be exponentially more efficient than a (shallow) convolutional network with one hidden layer. Using theoretical results on the relation between the tensor decompositions we compare expressive powers of the HT- and TT-Networks. We also implement the recurrent TT-Networks and provide numerical evidence of their expressivity.

Connected through hyperedge HE-1 (induced by source paper "learning the mapping mathbf x mapsto sum_ i 1 d x_i 2 the cost of finding the needle in a haystack"):
  with members:
    - "explaining landscape connectivity of low cost solutions for multilayer nets"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "barron spaces and the compositional function spaces for neural network models"
    - "a priori estimates of the population risk for residual networks"
    - "deep sets"
    - "rethinking the value of network pruning"
    - "error bounds for approximations with deep relu networks"
    - "learning functions when is deep better than shallow"

Connected through hyperedge HE-2 (induced by source paper "supervised learning for non sequential data with the canonical polyadic decomposition"):
  with members:
    - "tensornetwork for machine learning"
    - "tucker tensor layer in fully connected neural networks"
    - "generalized tensor models for recurrent neural networks"
    - "wide compression tensor ring nets"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"
    - "tensorizing neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "on the expressive power of deep learning a tensor analysis"

Connected through hyperedge HE-3 (induced by source paper "tensorized random projections"):
  with members:
    - "oblivious sketching of high degree polynomial kernels"
    - "faster johnson lindenstrauss transforms via kronecker products"
    - "tensorizing neural networks"
    - "experiments with random projection"
    - "random projections for k means clustering"

Connected through hyperedge HE-4 (induced by source paper "lossless compression for 3dcnns based on tensor train decomposition"):
  with members:
    - "tensor train decomposition on tensorflow t3f"
    - "tensor decomposition for compressing recurrent neural network"
    - "text independent speaker verification using 3d convolutional neural networks"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "deformable convolutional networks"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "tensor train recurrent neural networks for video classification"
    - "compressing recurrent neural network with tensor train"

Connected through hyperedge HE-5 (induced by source paper "optimal function approximation with relu neural networks"):
  with members:
    - "universality of deep convolutional neural networks"
    - "resnet with one neuron hidden layers is a universal approximator"
    - "optimal approximation of continuous functions by very deep relu networks"
    - "the power of deeper networks for expressing natural functions"
    - "the expressive power of neural networks a view from the width"
    - "error bounds for approximations with deep relu networks"
    - "deep vs shallow networks an approximation theory perspective"
    - "benefits of depth in neural networks"

Connected through hyperedge HE-6 (induced by source paper "tucker decomposition network expressive power and comparison"):
  with members:
    - "convolutional rectifier networks as generalized tensor decompositions"
    - "benefits of depth in neural networks"
    - "the power of depth for feedforward neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "on the expressive power of deep learning a tensor analysis"
    - "going deeper with convolutions"
    - "on the expressive efficiency of sum product networks"

Connected through hyperedge HE-7 (induced by source paper "radix net structured sparse matrices for deep neural networks"):
  with members:
    - "sparse deep neural network exact solutions"
    - "deep expander networks efficient deep networks from graph theory"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "the expressive power of neural networks a view from the width"
    - "enabling massive deep neural networks with the graphblas"
    - "training sparse neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "data free parameter pruning for deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "generalized tensor models for recurrent neural networks"):
  with members:
    - "tensor train recurrent neural networks for video classification"
    - "long term forecasting using higher order tensor rnns"
    - "tensor networks for dimensionality reduction and large scale optimization part 2 applications and future perspectives"
    - "tensorizing neural networks"
    - "on the expressive power of deep learning a tensor 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.LG(Machine Learning)