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: "rate distortion auto encoders"
  Abstract: A rekindled the interest in auto-encoder algorithms has been spurred by recent work on deep learning. Current efforts have been directed towards effective training of auto-encoder architectures with a large number of coding units. Here, we propose a learning algorithm for auto-encoders based on a rate-distortion objective that minimizes the mutual information between the inputs and the outputs of the auto-encoder subject to a fidelity constraint. The goal is to learn a representation that is minimally committed to the input data, but that is rich enough to reconstruct the inputs up to certain level of distortion. Minimizing the mutual information acts as a regularization term whereas the fidelity constraint can be understood as a risk functional in the conventional statistical learning setting. The proposed algorithm uses a recently introduced measure of entropy based on infinitely divisible matrices that avoids the plug in estimation of densities. Experiments using over-complete bases show that the rate-distortion auto-encoders can learn a regularized input-output mapping in an implicit manner.

Connected through hyperedge HE-1 (induced by source paper "information theoretic lower bounds on negative log likelihood"):
  with members:
    - "fixing a broken elbo"
    - "opening the black box of deep neural networks via information"
    - "vae with a vampprior"
    - "improving variational auto encoders using householder flow"
    - "pixel recurrent neural networks"
    - "importance weighted autoencoders"
    - "generating sentences from a continuous space"
    - "deep learning and the information bottleneck principle"

Connected through hyperedge HE-2 (induced by source paper "understanding autoencoders with information theoretic concepts"):
  with members:
    - "understanding deep learning generalization by maximum entropy"
    - "an introduction to deep learning for the physical layer"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "interpreting cnn knowledge via an explanatory graph"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "opening the black box of deep neural networks via information"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "estimating mixture entropy with pairwise distances"

Connected through hyperedge HE-3 (induced by source paper "context aware generative adversarial privacy"):
  with members:
    - "estimation efficiency under privacy constraints"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "information theoretic privacy with general distortion constraints"
    - "ensemble estimation of mutual information"
    - "deeprotect enabling inference based access control on mobile sensing applications"
    - "information potential auto encoders"
    - "privacy aware mmse estimation"
    - "minimax filter learning to preserve privacy from inference attacks"

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)