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: "matrix neural networks"
  Abstract: Traditional neural networks assume vectorial inputs as the network is arranged as layers of single line of computing units called neurons. This special structure requires the non-vectorial inputs such as matrices to be converted into vectors. This process can be problematic. Firstly, the spatial information among elements of the data may be lost during vectorisation. Secondly, the solution space becomes very large which demands very special treatments to the network parameters and high computational cost. To address these issues, we propose matrix neural networks (MatNet), which takes matrices directly as inputs. Each neuron senses summarised information through bilinear mapping from lower layer units in exactly the same way as the classic feed forward neural networks. Under this structure, back prorogation and gradient descent combination can be utilised to obtain network parameters efficiently. Furthermore, it can be conveniently extended for multimodal inputs. We apply MatNet to MNIST handwritten digits classification and image super resolution tasks to show its effectiveness. Without too much tweaking MatNet achieves comparable performance as the state-of-the-art methods in both tasks with considerably reduced complexity.

Connected through hyperedge HE-1 (induced by source paper "two dimensional stochastic configuration networks for image data analytics"):
  with members:
    - "learning less overlapping representations"
    - "recovery guarantees for one hidden layer neural networks"
    - "stochastic configuration networks ensemble with heterogeneous features for large scale data analytics"
    - "deep stochastic configuration networks with universal approximation property"
    - "approximation with random bases"
    - "matrix variate rbm model with gaussian distributions"
    - "on the generalization error bounds of neural networks under diversity inducing mutual angular regularization"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-2 (induced by source paper "temporal attention augmented bilinear network for financial time series data analysis"):
  with members:
    - "benchmark dataset for mid price forecasting of limit order book data with machine learning methods"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "learning deep matrix representations"
    - "lstm a search space odyssey"
    - "benchmark dataset for mid price prediction of limit order book data"
    - "multilinear class specific discriminant analysis"
    - "position based content attention for time series forecasting with sequence to sequence rnns"
    - "tensor representation in high frequency financial data for price change prediction"

Connected through hyperedge HE-3 (induced by source paper "learning deep matrix representations"):
  with members:
    - "gated graph sequence neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "highway and residual networks learn unrolled iterative estimation"
    - "unsupervised learning of video representations using lstms"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "grid long short term memory"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "individual recognition in schizophrenia using deep learning methods with random forest and voting classifiers insights from resting state eeg streams"):
  with members:
    - "deep residual networks with exponential linear unit"
    - "deep learning in neural networks"
    - "deep learning using linear support vector machines"

Connected through hyperedge HE-5 (induced by source paper "a riemannian network for spd matrix learning"):
  with members:
    - "practical riemannian neural networks"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "from manifold to manifold geometry aware dimensionality reduction for spd matrices"
    - "riemannian metrics for neural networks i feedforward networks"
    - "spatio temporal covariance descriptors for action and gesture recognition"
    - "spectral networks and locally connected networks on graphs"

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)