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 kernels for probability distributions"
  Abstract: Optimal transport distances, otherwise known as Wasserstein distances, have recently drawn ample attention in computer vision and machine learning as a powerful discrepancy measure for probability distributions. The recent developments on alternative formulations of the optimal transport have allowed for faster solutions to the problem and has revamped its practical applications in machine learning. In this paper, we exploit the widely used kernel methods and provide a family of provably positive definite kernels based on the Sliced Wasserstein distance and demonstrate the benefits of these kernels in a variety of learning tasks. Our work provides a new perspective on the application of optimal transport flavored distances through kernel methods in machine learning tasks.

Connected through hyperedge HE-1 (induced by source paper "learning autoencoders with relational regularization"):
  with members:
    - "hierarchical optimal transport for document representation"
    - "scalable gromov wasserstein learning for graph partitioning and matching"
    - "learning generative models across incomparable spaces"
    - "gromov wasserstein factorization models for graph clustering"
    - "gromov wasserstein alignment of word embedding spaces"
    - "sliced wasserstein autoencoder an embarrassingly simple generative model"
    - "the gromov wasserstein distance between networks and stable network invariants"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"

Connected through hyperedge HE-2 (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"
    - "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"
    - "regularized discrete optimal transport"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-3 (induced by source paper "sliced wasserstein generative models"):
  with members:
    - "video to video synthesis"
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "spectral normalization for generative adversarial networks"
    - "improving gans using optimal transport"
    - "banach wasserstein gan"
    - "generative modeling using the sliced wasserstein distance"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-4 (induced by source paper "conservative wasserstein training for pose estimation"):
  with members:
    - "unsupervised domain adaptation via calibrating uncertainties"
    - "dependency aware attention control for unconstrained face recognition with image sets"
    - "deep verifier networks verification of deep discriminative models with deep generative models"
    - "confidence regularized self training"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "a mixed classification regression framework for 3d pose estimation from 2d images"
    - "starmap for category agnostic keypoint and viewpoint estimation"
    - "deep directional statistics pose estimation with uncertainty quantification"

Connected through hyperedge HE-5 (induced by source paper "illegible text to readable text an image to image transformation using conditional sliced wasserstein adversarial networks"):
  with members:
    - "sliced wasserstein generative models"
    - "generalized sliced wasserstein distances"
    - "handwritten text segmentation via end to end learning of convolutional neural network"
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "how good is my gan"
    - "progressive growing of gans for improved quality stability and variation"
    - "improved training of wasserstein gans"
    - "improved techniques for training gans"

Connected through hyperedge HE-6 (induced by source paper "wasserstein weisfeiler lehman graph kernels"):
  with members:
    - "gromov wasserstein learning for graph matching and node embedding"
    - "on the definiteness of earth mover s distance and its relation to set intersection"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "semi supervised classification with graph convolutional networks"
    - "on valid optimal assignment kernels and applications to graph classification"
    - "faster kernels for graphs with continuous attributes via hashing"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "learning with a wasserstein loss"

Connected through hyperedge HE-7 (induced by source paper "representation learning with weighted inner product for universal approximation of general similarities"):
  with members:
    - "skip gram word embeddings in hyperbolic space"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "poincar e embeddings for learning hierarchical representations"
    - "neural networks and rational functions"
    - "error bounds for approximations with deep relu networks"
    - "a primer on neural network models for natural language processing"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-8 (induced by source paper "wasserstein neural processes"):
  with members:
    - "generalized sliced wasserstein distances"
    - "attentive neural processes"
    - "sliced wasserstein autoencoder an embarrassingly simple generative model"
    - "conditional neural processes"
    - "improved training of wasserstein gans"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

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)