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: "sliced wasserstein distance for learning gaussian mixture models"
  Abstract: Gaussian mixture models (GMM) are powerful parametric tools with many applications in machine learning and computer vision. Expectation maximization (EM) is the most popular algorithm for estimating the GMM parameters. However, EM guarantees only convergence to a stationary point of the log-likelihood function, which could be arbitrarily worse than the optimal solution. Inspired by the relationship between the negative log-likelihood function and the Kullback-Leibler (KL) divergence, we propose an alternative formulation for estimating the GMM parameters using the sliced Wasserstein distance, which gives rise to a new algorithm. Specifically, we propose minimizing the sliced-Wasserstein distance between the mixture model and the data distribution with respect to the GMM parameters. In contrast to the KL-divergence, the energy landscape for the sliced-Wasserstein distance is more well-behaved and therefore more suitable for a stochastic gradient descent scheme to obtain the optimal GMM parameters. We show that our formulation results in parameter estimates that are more robust to random initializations and demonstrate that it can estimate high-dimensional data distributions more faithfully than the EM algorithm.

Connected through hyperedge HE-1 (induced by source paper "regularized wasserstein means for aligning distributional data"):
  with members:
    - "a gradual semi discrete approach to generative network training via explicit wasserstein minimization"
    - "variational wasserstein clustering"
    - "go icp a globally optimal solution to 3d icp point set registration"
    - "deep coral correlation alignment for deep domain adaptation"
    - "facenet a unified embedding for face recognition and clustering"
    - "sliced wasserstein kernels for probability distributions"
    - "regularized discrete optimal transport"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-2 (induced by source paper "co generation with gans using ais based hmc"):
  with members:
    - "max sliced wasserstein distance and its use for gans"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "improving gans using optimal transport"
    - "generative modeling using the sliced wasserstein distance"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "mcgan mean and covariance feature matching gan"

Connected through hyperedge HE-3 (induced by source paper "sliced wasserstein discrepancy for unsupervised domain adaptation"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "variational wasserstein clustering"
    - "learning to adapt structured output space for semantic segmentation"
    - "syn2real a new benchmark forsynthetic to real visual domain adaptation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "open set domain adaptation by backpropagation"
    - "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"
    - "generative modeling using the sliced wasserstein distance"

Connected through hyperedge HE-4 (induced by source paper "max sliced wasserstein distance and its use for gans"):
  with members:
    - "sliced wasserstein generative models"
    - "sliced wasserstein autoencoder an embarrassingly simple generative model"
    - "multimodal unsupervised image to image translation"
    - "improving gans using optimal transport"
    - "generative modeling using the sliced wasserstein distance"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "which training methods for gans do actually converge"
    - "mcgan mean and covariance feature matching gan"

Connected through hyperedge HE-5 (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"
    - "cramer wold autoencoder"
    - "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"
    - "good semi supervised learning that requires a bad gan"

Connected through hyperedge HE-6 (induced by source paper "generalized sliced wasserstein distances"):
  with members:
    - "generative modeling using the sliced wasserstein distance"
    - "progressive growing of gans for improved quality stability and variation"
    - "sliced wasserstein kernel for persistence diagrams"
    - "sliced wasserstein kernels for probability distributions"
    - "learning with a wasserstein loss"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "complementary learning for overcoming catastrophic forgetting using experience replay"):
  with members:
    - "selective experience replay for lifelong learning"
    - "memory aware synapses learning what not to forget"
    - "continual learning with deep generative replay"
    - "continual learning through synaptic intelligence"
    - "stabilizing training of generative adversarial networks through regularization"
    - "prioritized experience replay"

Connected through hyperedge HE-8 (induced by source paper "x ray sobolev variational auto encoders"):
  with members:
    - "sliced wasserstein generative models"
    - "max sliced wasserstein distance and its use for gans"
    - "generative modeling using the sliced wasserstein distance"
    - "adam a method for stochastic optimization"

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.CV(Computer Vision and Pattern Recognition)