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: "exact rate distortion in autoencoders via echo noise"
  Abstract: Compression is at the heart of effective representation learning. However, lossy compression is typically achieved through simple parametric models like Gaussian noise to preserve analytic tractability, and the limitations this imposes on learning are largely unexplored. Further, the Gaussian prior assumptions in models such as variational autoencoders (VAEs) provide only an upper bound on the compression rate in general. We introduce a new noise channel, Echo noise, that admits a simple, exact expression for mutual information for arbitrary input distributions. The noise is constructed in a data-driven fashion that does not require restrictive distributional assumptions. With its complex encoding mechanism and exact rate regularization, Echo leads to improved bounds on log-likelihood and dominates $\beta$-VAEs across the achievable range of rate-distortion trade-offs. Further, we show that Echo noise can outperform state-of-the-art flow methods without the need to train complex distributional transformations

Connected through hyperedge HE-1 (induced by source paper "a free energy principle for representation learning"):
  with members:
    - "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"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-2 (induced by source paper "hierarchical quantized autoencoders"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "hierarchical autoregressive image models with auxiliary decoders"
    - "rethinking lossy compression the rate distortion perception tradeoff"
    - "the perception distortion tradeoff"
    - "deep generative models for distribution preserving lossy compression"
    - "recent advances in autoencoder based representation learning"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "variational memory addressing in generative models"

Connected through hyperedge HE-3 (induced by source paper "rate distortion optimization guided autoencoder for isometric embedding in euclidean latent space"):
  with members:
    - "adversarially learned anomaly detection"
    - "recent advances in autoencoder based representation learning"
    - "variational autoencoders pursue pca directions by accident"
    - "fixing a broken elbo"
    - "the riemannian geometry of deep generative models"
    - "adam a method for stochastic optimization"
    - "nice non linear independent components estimation"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-4 (induced by source paper "discovery and separation of features for invariant representation learning"):
  with members:
    - "unified adversarial invariance"
    - "invariant representations through adversarial forgetting"
    - "ropad robust presentation attack detection through unsupervised adversarial invariance"
    - "niesr nuisance invariant end to end speech recognition"
    - "invariant representations without adversarial training"
    - "unsupervised adversarial invariance"
    - "information constraints on auto encoding variational bayes"
    - "emergence of invariance and disentanglement in deep representations"

Connected through hyperedge HE-5 (induced by source paper "rate distortion optimization guided autoencoder for generative approach with quantitatively measurable latent space"):
  with members:
    - "adversarially learned anomaly detection"
    - "recent advances in autoencoder based representation learning"
    - "variational autoencoders pursue pca directions by accident"
    - "neural discrete representation learning"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "density modeling of images using a generalized normalization transformation"
    - "adam a method for stochastic optimization"
    - "nice non linear independent components estimation"

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)