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: "variational wasserstein clustering"
  Abstract: We propose a new clustering method based on optimal transportation. We solve optimal transportation with variational principles, and investigate the use of power diagrams as transportation plans for aggregating arbitrary domains into a fixed number of clusters. We iteratively drive centroids through target domains while maintaining the minimum clustering energy by adjusting the power diagrams. Thus, we simultaneously pursue clustering and the Wasserstein distances between the centroids and the target domains, resulting in a measure-preserving mapping. We demonstrate the use of our method in domain adaptation, remeshing, and representation learning on synthetic and real data.

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"
    - "sliced wasserstein distance for learning gaussian mixture models"
    - "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 "variational wasserstein barycenters for geometric clustering"):
  with members:
    - "regularized wasserstein means for aligning distributional data"
    - "pytorch an imperative style high performance deep learning library"
    - "stochastic wasserstein barycenters"
    - "regularized wasserstein means based on variational transportation"
    - "parallel streaming wasserstein barycenters"
    - "multiscale strategies for computing optimal transport"

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"
    - "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"
    - "image to image translation for domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "deep discriminative clustering analysis"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "clustergan latent space clustering in generative adversarial networks"
    - "deep continuous clustering"
    - "relational inductive biases deep learning and graph networks"
    - "learning markov clustering networks for scene text detection"
    - "hierarchical clustering with structural constraints"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "learning from video and text via large scale discriminative clustering"

Connected through hyperedge HE-5 (induced by source paper "are few shot learning benchmarks too simple"):
  with members:
    - "multi class classification without multi class labels"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "regularized wasserstein means based on variational transportation"
    - "unsupervised learning via meta learning"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "learning to cluster in order to transfer across domains and tasks"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-6 (induced by source paper "regularized wasserstein means based on variational transportation"):
  with members:
    - "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "deep coral correlation alignment for deep domain adaptation"
    - "sliced wasserstein kernels for probability distributions"

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)