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: "learning discrete and continuous factors of data via alternating disentanglement"
  Abstract: We address the problem of unsupervised disentanglement of discrete and continuous explanatory factors of data. We first show a simple procedure for minimizing the total correlation of the continuous latent variables without having to use a discriminator network or perform importance sampling, via cascading the information flow in the $\beta$-vae framework. Furthermore, we propose a method which avoids offloading the entire burden of jointly modeling the continuous and discrete factors to the variational encoder by employing a separate discrete inference procedure. #R##N#This leads to an interesting alternating minimization problem which switches between finding the most likely discrete configuration given the continuous factors and updating the variational encoder based on the computed discrete factors. Experiments show that the proposed method clearly disentangles discrete factors and significantly outperforms current disentanglement methods based on the disentanglement score and inference network classification score. The source code is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "semi supervised disentanglement with independent vector variational autoencoders"):
  with members:
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "auto encoding total correlation explanation"
    - "disentangling latent space for vae by label relevant irrelevant dimensions"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-2 (induced by source paper "skill transfer in deep reinforcement learning under morphological heterogeneity"):
  with members:
    - "exploiting hierarchy for learning and transfer in kl regularized rl"
    - "synthesized policies for transfer and adaptation across tasks and environments"
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "policy transfer with strategy optimization"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "visual reinforcement learning with imagined goals"
    - "bayesian model agnostic meta learning"

Connected through hyperedge HE-3 (induced by source paper "variation network learning high level attributes for controlled input manipulation"):
  with members:
    - "lipschitz generative adversarial nets"
    - "diagnosing and enhancing vae models"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "deep learning based decoding for constrained sequence codes"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "rednet residual encoder decoder network for indoor rgb d semantic segmentation"
    - "sampling generative networks"
    - "glsr vae geodesic latent space regularization for variational autoencoder architectures"

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)