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 "trivializations for gradient based optimization on manifolds"):
  Abstract: We introduce a framework to study the transformation of problems with manifold constraints into unconstrained problems through parametrizations in terms of a Euclidean space. We call these parametrizations "trivializations". We prove conditions under which a trivialization is sound in the context of gradient-based optimization and we show how two large families of trivializations have overall favorable properties, but also suffer from a performance issue. We then introduce "dynamic trivializations", which solve this problem, and we show how these form a family of optimization methods that lie between trivializations and Riemannian gradient descent, and combine the benefits of both of them. We then show how to implement these two families of trivializations in practice for different matrix manifolds. To this end, we prove a formula for the gradient of the exponential of matrices, which can be of practical interest on its own. Finally, we show how dynamic trivializations improve the performance of existing methods on standard tasks designed to test long-term memory within neural networks.

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 "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 "stabilizing gradients for deep neural networks via efficient svd parameterization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalization and representational limits of graph neural networks")
  Member "improving variational auto encoders using householder flow":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "augmented normalizing flows bridging the gap between generative flows and latent variable models")
  Member "efficient orthogonal parametrisation of recurrent neural networks using householder reflections":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "tunable efficient unitary neural networks eunn and their application to rnns":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "differentiate everything with a reversible programming language")
  Member "a simple way to initialize recurrent networks of rectified linear units":
    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)