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 "eigenvalue normalized recurrent neural networks for short term memory"):
  Abstract: Several variants of recurrent neural networks (RNNs) with orthogonal or unitary recurrent matrices have recently been developed to mitigate the vanishing/exploding gradient problem and to model long-term dependencies of sequences. However, with the eigenvalues of the recurrent matrix on the unit circle, the recurrent state retains all input information which may unnecessarily consume model capacity. In this paper, we address this issue by proposing an architecture that expands upon an orthogonal/unitary RNN with a state that is generated by a recurrent matrix with eigenvalues in the unit disc. Any input to this state dissipates in time and is replaced with new inputs, simulating short-term memory. A gradient descent algorithm is derived for learning such a recurrent matrix. The resulting method, called the Eigenvalue Normalized RNN (ENRNN), is shown to be highly competitive in several experiments.

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 "non normal recurrent neural network nnrnn learning long time dependencies while improving expressivity with transient dynamics":
    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 "non normal recurrent neural network nnrnn learning long time dependencies while improving expressivity with transient dynamics")
  Member "stabilizing gradients for deep neural networks via efficient svd parameterization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generalization and representational limits of graph neural networks")
  Member "complex gated recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fast fourier forecasting resource utilisation in distributed systems")
  Member "gated orthogonal recurrent units on learning to forget":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generating high fidelity synthetic time series datasets with doppelganger")
  Member "fast slow recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "autofoley artificial synthesis of synchronized sound tracks for silent videos with deep learning")
  Member "on orthogonality and learning recurrent networks with long term dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "concept whitening for interpretable image recognition")

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)