human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "loss landscapes of regularized linear autoencoders"
  Abstract: Autoencoders are a deep learning model for representation learning. When trained to minimize the distance between the data and its reconstruction, linear autoencoders (LAEs) learn the subspace spanned by the top principal directions but cannot learn the principal directions themselves. In this paper, we prove that $L_2$-regularized LAEs are symmetric at all critical points and learn the principal directions as the left singular vectors of the decoder. We smoothly parameterize the critical manifold and relate the minima to the MAP estimate of probabilistic PCA. We illustrate these results empirically and consider implications for PCA algorithms, computational neuroscience, and the algebraic topology of learning.

Connected through hyperedge HE-1 (induced by source paper "a linear systems theory of normalizing flows"):
  with members:
    - "hybrid models with deep and invertible features"
    - "emerging convolutions for generative normalizing flows"
    - "density estimation using real nvp"
    - "nice non linear independent components estimation"

Connected through hyperedge HE-2 (induced by source paper "theory iii dynamics and generalization in deep networks a simple solution"):
  with members:
    - "mean field limit of the learning dynamics of multilayer neural networks"
    - "gradient descent maximizes the margin of homogeneous neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"
    - "gradient descent provably optimizes over parameterized neural networks"

Connected through hyperedge HE-3 (induced by source paper "don t blame the elbo a linear vae perspective on posterior collapse"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "diagnosing and enhancing vae models"
    - "preventing posterior collapse with delta vaes"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "isolating sources of disentanglement in variational autoencoders"
    - "inference suboptimality in variational autoencoders"
    - "variational autoencoders pursue pca directions by accident"
    - "improving explorability in variational inference with annealed variational objectives"

Connected through hyperedge HE-4 (induced by source paper "deep learning without weight transport"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "unsupervised learning by competing hidden units"
    - "direct feedback alignment with sparse connections for local learning"
    - "biologically plausible learning algorithms can scale to large datasets"
    - "feedback alignment in deep convolutional networks"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "understanding reuse performance and hardware cost of dnn dataflows a data centric approach"
    - "deep residual learning for image recognition"

Question: Please tell me which class the center node 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)