human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "non normal recurrent neural network nnrnn learning long time dependencies while improving expressivity with transient dynamics"):
  Abstract: A recent strategy to circumvent the exploding and vanishing gradient problem in RNNs, and to allow the stable propagation of signals over long time scales, is to constrain recurrent connectivity matrices to be orthogonal or unitary. This ensures eigenvalues with unit norm and thus stable dynamics and training. However this comes at the cost of reduced expressivity due to the limited variety of orthogonal transformations. We propose a novel connectivity structure based on the Schur decomposition and a splitting of the Schur form into normal and non-normal parts. This allows to parametrize matrices with unit-norm eigenspectra without orthogonality constraints on eigenbases. The resulting architecture ensures access to a larger space of spectrally constrained matrices, of which orthogonal matrices are a subset. This crucial difference retains the stability advantages and training speed of orthogonal RNNs while enhancing expressivity, especially on tasks that require computations over ongoing input sequences.

Includes members:
  Member "cheap orthogonal constraints in neural networks a simple parametrization of the orthogonal and unitary group":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "concept whitening for interpretable image recognition")
  Member "towards non saturating recurrent units for modelling long term dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "improved memory in recurrent neural networks with sequential non normal dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "eigenvalue normalized recurrent neural networks for short term memory")
  Member "independently recurrent neural network indrnn building a longer and deeper rnn":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "stabilizing gradients for deep neural networks via efficient svd parameterization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generalization and representational limits of graph neural networks")
  Member "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a comprehensive and modularized statistical framework for gradient norm equality in deep neural networks")
  Member "efficient orthogonal parametrisation of recurrent neural networks using householder reflections":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "concept whitening for interpretable image recognition")
  Member "tunable efficient unitary neural networks eunn and their application to rnns":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "differentiate everything with a reversible programming language")

Question: Please tell me which class the source paper 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)