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: "bounded information rate variational autoencoders"
  Abstract: This paper introduces a new member of the family of Variational Autoencoders (VAE) that constrains the rate of information transferred by the latent layer. The latent layer is interpreted as a communication channel, the information rate of which is bound by imposing a pre-set signal-to-noise ratio. The new constraint subsumes the mutual information between the input and latent variables, combining naturally with the likelihood objective of the observed data as used in a conventional VAE. The resulting Bounded-Information-Rate Variational Autoencoder (BIR-VAE) provides a meaningful latent representation with an information resolution that can be specified directly in bits by the system designer. The rate constraint can be used to prevent overtraining, and the method naturally facilitates quantisation of the latent variables at the set rate. Our experiments confirm that the BIR-VAE has a meaningful latent representation and that its performance is at least as good as state-of-the-art competing algorithms, but with lower computational complexity.

Connected through hyperedge HE-1 (induced by source paper "noise contrastive variational autoencoders"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "contrastive variational autoencoder enhances salient features"
    - "preventing posterior collapse with delta vaes"
    - "mae mutual posterior divergence regularization for variational autoencoders"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "infovae information maximizing variational autoencoders"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "fixing a broken elbo"

Connected through hyperedge HE-2 (induced by source paper "exact rate distortion in autoencoders via echo noise"):
  with members:
    - "isolating sources of disentanglement in variational autoencoders"
    - "auto encoding total correlation explanation"
    - "recent advances in autoencoder based representation learning"
    - "mine mutual information neural estimation"
    - "infovae information maximizing variational autoencoders"
    - "disentangled representations via synergy minimization"
    - "fixing a broken elbo"
    - "vae with a vampprior"

Connected through hyperedge HE-3 (induced by source paper "gaussian mean field regularizes by limiting learned information"):
  with members:
    - "tightening mutual information based bounds on generalization error"
    - "implicit regularization of stochastic gradient descent in natural language processing observations and implications"
    - "neural ordinary differential equations"
    - "mine mutual information neural estimation"
    - "representation learning with contrastive predictive coding"
    - "parameter space noise for exploration"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "emergence of invariance and disentanglement in deep representations"

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)