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: "learning input and recurrent weight matrices in echo state networks"
  Abstract: Echo State Networks (ESNs) are a special type of the temporally deep network model, the Recurrent Neural Network (RNN), where the recurrent matrix is carefully designed and both the recurrent and input matrices are fixed. An ESN uses the linearity of the activation function of the output units to simplify the learning of the output matrix. In this paper, we devise a special technique that take advantage of this linearity in the output units of an ESN, to learn the input and recurrent matrices. This has not been done in earlier ESNs due to their well known difficulty in learning those matrices. Compared to the technique of BackPropagation Through Time (BPTT) in learning general RNNs, our proposed method exploits linearity of activation function in the output units to formulate the relationships amongst the various matrices in an RNN. These relationships results in the gradient of the cost function having an analytical form and being more accurate. This would enable us to compute the gradients instead of obtaining them by recursion as in BPTT. Experimental results on phone state classification show that learning one or both the input and recurrent matrices in an ESN yields superior results compared to traditional ESNs that do not learn these matrices, especially when longer time steps are used.

Connected through hyperedge HE-1 (induced by source paper "the power of linear recurrent neural networks"):
  with members:
    - "analysing soccer games with clustering and conceptors"
    - "difference between memory and prediction in linear recurrent networks"
    - "a critical review of recurrent neural networks for sequence learning"
    - "training recurrent networks online without backtracking"
    - "controlling recurrent neural networks by conceptors"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "distributed compressive sensing a deep learning approach"):
  with members:
    - "two dimensional pattern coupled sparse bayesian learning via generalized approximate message passing"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "a deep learning approach to structured signal recovery"
    - "sequence transduction with recurrent neural networks"
    - "advances in optimizing recurrent networks"
    - "structured compressed sensing from theory to applications"
    - "ls cs residual ls cs compressive sensing on least squares residual"
    - "model based compressive sensing"

Connected through hyperedge HE-3 (induced by source paper "improving performance of recurrent neural network with relu nonlinearity"):
  with members:
    - "lstm a search space odyssey"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "beyond short snippets deep networks for video classification"
    - "going deeper with convolutions"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "a clockwork rnn"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)