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 "cheap orthogonal constraints in neural networks a simple parametrization of the orthogonal and unitary group"):
  Abstract: We introduce a novel approach to perform first-order optimization with orthogonal and unitary constraints. This approach is based on a parametrization stemming from Lie group theory through the exponential map. The parametrization transforms the constrained optimization problem into an unconstrained one over a Euclidean space, for which common first-order optimization methods can be used. The theoretical results presented are general enough to cover the special orthogonal group, the unitary group and, in general, any connected compact Lie group. We discuss how this and other parametrizations can be computed efficiently through an implementation trick, making numerically complex parametrizations usable at a negligible runtime cost in neural networks. In particular, we apply our results to RNNs with orthogonal recurrent weights, yielding a new architecture called expRNN. We demonstrate how our method constitutes a more robust approach to optimization with orthogonal constraints, showing faster, accurate, and more stable convergence in several tasks designed to test RNNs.

Includes members:
  Member "riemannian stochastic variance reduced gradient algorithm with retraction and vector transport":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adaptive stochastic gradient algorithms on riemannian manifolds")
  Member "riemannian adaptive optimization methods":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient riemannian optimization on the stiefel manifold via the cayley transform")
  Member "can we gain more from orthogonality regularizations in training deep cnns":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self orthogonality module a network architecture plug in for learning orthogonal filters")
  Member "orthogonal weight normalization solution to optimization over multiple dependent stiefel manifolds in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "on orthogonality and learning recurrent networks with long term dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "concept whitening for interpretable image recognition")
  Member "generalized backpropagation etude de cas orthogonality":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "concept whitening for interpretable image recognition")
  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 "recurrent orthogonal networks and long memory tasks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")

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)