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 "dimensionality compression and expansion in deep neural networks"):
  Abstract: Datasets such as images, text, or movies are embedded in high-dimensional spaces. However, in important cases such as images of objects, the statistical structure in the data constrains samples to a manifold of dramatically lower dimensionality. Learning to identify and extract task-relevant variables from this embedded manifold is crucial when dealing with high-dimensional problems. We find that neural networks are often very effective at solving this task and investigate why. To this end, we apply state-of-the-art techniques for intrinsic dimensionality estimation to show that neural networks learn low-dimensional manifolds in two phases: first, dimensionality expansion driven by feature generation in initial layers, and second, dimensionality compression driven by the selection of task-relevant features in later layers. We model noise generated by Stochastic Gradient Descent and show how this noise balances the dimensionality of neural representations by inducing an effective regularization term in the loss. We highlight the important relationship between low-dimensional compressed representations and generalization properties of the network. Our work contributes by shedding light on the success of deep neural networks in disentangling data in high-dimensional space while achieving good generalization. Furthermore, it invites new learning strategies focused on optimizing measurable geometric properties of learned representations, beginning with their intrinsic dimensionality.

Includes members:
  Member "geometry of deep generative models for disentangled representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "disentangling representations using gaussian processes in variational autoencoders for video prediction")
  Member "the local dimension of deep manifold":
    also connected through other hyperedges:
      (none)
  Member "energy entropy competition and the effectiveness of stochastic gradient descent in machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mga momentum gradient attack on network")
  Member "understanding convolutional neural networks with information theory an initial exploration":
    also connected through other hyperedges:
      (none)
  Member "fisher rao metric geometry and complexity of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the role of invariance in spectral complexity based generalization bounds")
  Member "towards understanding generalization of deep learning perspective of loss landscapes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a diffusion theory for deep learning dynamics stochastic gradient descent escapes from sharp minima exponentially fast")
  Member "the riemannian geometry of deep generative models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on implicit regularization in beta vaes")
  Member "a bayesian perspective on generalization and stochastic gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the implicit and explicit regularization effects of dropout")

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)