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: "on the emergence of invariance and disentangling in deep representations"
  Abstract: Using classical notions of statistical decision and information theory, we show that invariance in a deep neural network is equivalent to minimality of the representation it computes, and can be achieved by stacking layers and injecting noise in the computation, under realistic and empirically validated assumptions. We use an Information Decomposition of the empirical loss to show that overfitting can be reduced by limiting the information content stored in the weights. We then present a sharp inequality that relates the information content in the weights -- which are a representation of the training set and inferred by generic optimization agnostic of invariance and disentanglement -- and the minimality and total correlation of the activation functions, which are a representation of the test datum. This allows us to tackle recent puzzles concerning the generalization properties of deep networks and their relation to the geometry of the optimization residual.

Connected through hyperedge HE-1 (induced by source paper "a free energy principle for representation learning"):
  with members:
    - "exact rate distortion in autoencoders via echo noise"
    - "therml the thermodynamics of machine learning"
    - "taskonomy disentangling task transfer learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "emergence of invariance and disentanglement in deep representations"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "fixing a broken elbo"
    - "multi task self supervised visual learning"

Connected through hyperedge HE-2 (induced by source paper "on universal features for high dimensional learning and inference"):
  with members:
    - "on the sample complexity of hgr maximal correlation functions"
    - "probabilistic clustering using maximal matrix norm couplings"
    - "an information theoretic framework for non linear canonical correlation analysis"
    - "deep generalized canonical correlation analysis"
    - "strong data processing inequalities for channels and bayesian networks"
    - "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"
    - "principal inertia components and applications"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-3 (induced by source paper "information bottleneck and its applications in deep learning"):
  with members:
    - "a direct sum result for the information complexity of learning"
    - "chaining mutual information and tightening generalization bounds"
    - "mathematics of deep learning"
    - "infovae information maximizing variational autoencoders"
    - "emergence of invariance and disentanglement in deep representations"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "fixing a broken elbo"
    - "sharp minima can generalize for deep nets"

Connected through hyperedge HE-4 (induced by source paper "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization"):
  with members:
    - "stochastic gradient mirror descent minimax optimality and implicit regularization"
    - "gradient descent finds global minima of deep neural networks"
    - "overparameterized nonlinear learning gradient descent takes the shortest path"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "a convergence theory for deep learning via over parameterization"
    - "geometry of optimization and implicit regularization in deep learning"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"

Connected through hyperedge HE-5 (induced by source paper "analyzing data selection techniques with tools from the theory of information losses"):
  with members:
    - "asymptotic finite sample information losses in neural classifiers"
    - "information losses in neural classifiers from sampling"
    - "interpreting active learning methods through information losses"
    - "approximation beats concentration an approximation view on inference with smooth radial kernels"
    - "mine mutual information neural estimation"
    - "active one shot learning"
    - "learning how to active learn a deep reinforcement learning approach"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-6 (induced by source paper "information losses in neural classifiers from sampling"):
  with members:
    - "mine mutual information neural estimation"
    - "opening the black box of deep neural networks via information"
    - "train faster generalize better stability of stochastic gradient descent"
    - "deep learning and the information bottleneck principle"
    - "openml networked science in machine learning"
    - "entropy bounds for discrete random variables via maximal coupling"

Connected through hyperedge HE-7 (induced by source paper "a geometric interpretation of stochastic gradient descent using diffusion metrics"):
  with members:
    - "emergence of invariance and disentanglement in deep representations"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "densely connected convolutional networks"
    - "trivializing the energy landscape of deep networks"
    - "on the energy landscape of deep networks"

Connected through hyperedge HE-8 (induced by source paper "a style based generator architecture for generative adversarial networks"):
  with members:
    - "cgans with projection discriminator"
    - "gaussian mixture generative adversarial networks for diverse datasets and the unsupervised clustering of images"
    - "generative adversarial interpolative autoencoding adversarial training on latent space interpolations encourages convex latent distributions"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "improved training with curriculum gans"
    - "isolating sources of disentanglement in variational autoencoders"
    - "large scale gan training for high fidelity natural image synthesis"

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)