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: "cramer wold autoencoder"
  Abstract: We propose a new generative model, Cramer-Wold Autoencoder (CWAE). Following WAE, we directly encourage normality of the latent space. Our paper uses also the recent idea from Sliced WAE (SWAE) model, which uses one-dimensional projections as a method of verifying closeness of two distributions. The crucial new ingredient is the introduction of a new (Cramer-Wold) metric in the space of densities, which replaces the Wasserstein metric used in SWAE. We show that the Cramer-Wold metric between Gaussian mixtures is given by a simple analytic formula, which results in the removal of sampling necessary to estimate the cost function in WAE and SWAE models. As a consequence, while drastically simplifying the optimization procedure, CWAE produces samples of a matching perceptual quality to other SOTA models.

Connected through hyperedge HE-1 (induced by source paper "towards photographic image manipulation with balanced growing of generative autoencoders"):
  with members:
    - "the gan landscape losses architectures regularization and normalization"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "pioneer networks progressively growing generative autoencoder"
    - "spectral normalization for generative adversarial networks"
    - "isolating sources of disentanglement in variational autoencoders"
    - "recursive chaining of reversible image to image translators for face aging"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-2 (induced by source paper "best of many samples distribution matching"):
  with members:
    - "joint wasserstein autoencoders for aligning multimodal embeddings"
    - "learning implicit generative models with the method of learned moments"
    - "mixture density generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "banach wasserstein gan"
    - "large scale gan training for high fidelity natural image synthesis"
    - "dist gan an improved gan using distance constraints"
    - "can vaes generate novel examples"

Connected through hyperedge HE-3 (induced by source paper "segma semi supervised gaussian mixture auto encoder"):
  with members:
    - "content and linguistic biases in the peer review process of artificial intelligence conferences"
    - "variational information bottleneck for unsupervised clustering deep gaussian mixture embedding"
    - "interpolation in generative models"
    - "generating sentences by editing prototypes"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "sliced wasserstein distance for learning gaussian mixture models"
    - "good semi supervised learning that requires a bad gan"

Connected through hyperedge HE-4 (induced by source paper "one element batch training by moving window"):
  with members:
    - "sliced generative models"
    - "generative adversarial networks recent developments"
    - "towards photographic image manipulation with balanced growing of generative autoencoders"
    - "sliced wasserstein autoencoder an embarrassingly simple generative model"
    - "pioneer networks progressively growing generative autoencoder"
    - "set aggregation network for structured data processing"
    - "revisiting small batch training for deep neural networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-5 (induced by source paper "non linear ica based on cramer wold metric"):
  with members:
    - "nice non linear independent components estimation"

Connected through hyperedge HE-6 (induced by source paper "pioneer networks progressively growing generative autoencoder"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "learning inverse mapping by autoencoder based generative adversarial nets"
    - "it takes only two adversarial generator encoder networks"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "stabilizing training of generative adversarial networks through regularization"

Connected through hyperedge HE-7 (induced by source paper "gaussian autoencoder"):
  with members:
    - "sliced wasserstein autoencoder an embarrassingly simple generative model"
    - "designing dedicated data compression for physics experiments within fpga already used for data acquisition"

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)