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: "geodesic exponential kernels when curvature and linearity conflict"
  Abstract: We consider kernel methods on general geodesic metric spaces and provide both negative and positive results. First we show that the common Gaussian kernel can only be generalized to a positive definite kernel on a geodesic metric space if the space is flat. As a result, for data on a Riemannian manifold, the geodesic Gaussian kernel is only positive definite if the Riemannian manifold is Euclidean. This implies that any attempt to design geodesic Gaussian kernels on curved Riemannian manifolds is futile. However, we show that for spaces with conditionally negative definite distances the geodesic Laplacian kernel can be generalized while retaining positive definiteness. This implies that geodesic Laplacian kernels can be generalized to some curved spaces, including spheres and hyperbolic spaces. Our theoretical results are verified empirically.

Connected through hyperedge HE-1 (induced by source paper "bayesian optimization meets riemannian manifolds in robot learning"):
  with members:
    - "riemannian motion policies"
    - "bayesian optimization with automatic prior selection for data efficient direct policy search"
    - "deep kernels for optimizing locomotion controllers"
    - "deep reinforcement learning a brief survey"
    - "bayesian optimization using domain knowledge on the atrias biped"
    - "on the design of lqr kernels for efficient controller learning"
    - "pymanopt a python toolbox for optimization on manifolds using automatic differentiation"
    - "learning dynamic robot to human object handover from human feedback"

Connected through hyperedge HE-2 (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"
    - "sliced wasserstein kernels for probability distributions"

Connected through hyperedge HE-3 (induced by source paper "poincare wasserstein autoencoder"):
  with members:
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "hyperbolic neural networks"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "infovae information maximizing variational autoencoders"
    - "vae with a vampprior"
    - "poincar e embeddings for learning hierarchical representations"
    - "neural discrete representation learning"
    - "density estimation using real nvp"

Connected through hyperedge HE-4 (induced by source paper "semi convolutional operators for instance segmentation"):
  with members:
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "segmentation aware convolutional networks using local attention masks"
    - "recurrent pixel embedding for instance grouping"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "semantic instance segmentation via deep metric learning"
    - "mask r cnn"
    - "instance sensitive fully convolutional networks"
    - "pixel level encoding and depth layering for instance level semantic labeling"

Connected through hyperedge HE-5 (induced by source paper "convex class model on symmetric positive definite manifolds"):
  with members:
    - "kernel sparse subspace clustering on symmetric positive definite manifolds"
    - "geometry aware similarity learning on spd manifolds for visual recognition"
    - "matching image sets via adaptive multi convex hull"
    - "kernel methods on the riemannian manifold of symmetric positive definite matrices"
    - "extrinsic methods for coding and dictionary learning on grassmann manifolds"
    - "sparse coding and dictionary learning for symmetric positive definite matrices a kernel approach"
    - "compressive acquisition of dynamic scenes"

Connected through hyperedge HE-6 (induced by source paper "dimensionality reduction on spd manifolds the emergence of geometry aware methods"):
  with members:
    - "a riemannian network for spd matrix learning"
    - "supervised logeuclidean metric learning for symmetric positive definite matrices"
    - "kernel methods on riemannian manifolds with gaussian rbf kernels"
    - "from manifold to manifold geometry aware dimensionality reduction for spd matrices"
    - "sparse coding on symmetric positive definite manifolds using bregman divergences"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "spatio temporal covariance descriptors for action and gesture recognition"

Connected through hyperedge HE-7 (induced by source paper "riemannian dictionary learning and sparse coding for positive definite matrices"):
  with members:
    - "kernel methods on riemannian manifolds with gaussian rbf kernels"
    - "from manifold to manifold geometry aware dimensionality reduction for spd matrices"
    - "sparse modeling for image and vision processing"
    - "sparse coding on symmetric positive definite manifolds using bregman divergences"
    - "dictionary learning and sparse coding on grassmann manifolds an extrinsic solution"
    - "manopt a matlab toolbox for optimization on manifolds"

Connected through hyperedge HE-8 (induced by source paper "sliced wasserstein kernels for probability distributions"):
  with members:
    - "on the definiteness of earth mover s distance and its relation to set intersection"
    - "the radon cumulative distribution transform and its application to image classification"
    - "the cumulative distribution transform and linear pattern classification"
    - "earth mover s distance yields positive definite kernels for certain ground distances"
    - "kernel methods on riemannian manifolds with gaussian rbf kernels"
    - "kernel methods on the riemannian manifold of symmetric positive definite matrices"

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)