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: "kernel methods on the riemannian manifold of symmetric positive definite matrices"
  Abstract: Symmetric Positive Definite (SPD) matrices have become popular to encode image information. Accounting for the geometry of the Riemannian manifold of SPD matrices has proven key to the success of many algorithms. However, most existing methods only approximate the true shape of the manifold locally by its tangent plane. In this paper, inspired by kernel methods, we propose to map SPD matrices to a high dimensional Hilbert space where Euclidean geometry applies. To encode the geometry of the manifold in the mapping, we introduce a family of provably positive definite kernels on the Riemannian manifold of SPD matrices. These kernels are derived from the Gaussian ker- nel, but exploit different metrics on the manifold. This lets us extend kernel-based algorithms developed for Euclidean spaces, such as SVM and kernel PCA, to the Riemannian manifold of SPD matrices. We demonstrate the benefits of our approach on the problems of pedestrian detection, ob- ject categorization, texture analysis, 2D motion segmentation and Diffusion Tensor Imaging (DTI) segmentation.

Connected through hyperedge HE-1 (induced by source paper "identity preserving face recovery from stylized portraits"):
  with members:
    - "recovering faces from portraits with auxiliary facial attributes"
    - "identity preserving face recovery from portraits"
    - "a deeper look at power normalizations"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "multi style generative network for real time transfer"
    - "high quality facial photo sketch synthesis using multi adversarial networks"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-2 (induced by source paper "recovering faces from portraits with auxiliary facial attributes"):
  with members:
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "identity preserving face recovery from portraits"
    - "a deeper look at power normalizations"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "high quality facial photo sketch synthesis using multi adversarial networks"
    - "diversified texture synthesis with feed forward networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"

Connected through hyperedge HE-3 (induced by source paper "optimal transport on the manifold of spd matrices for domain adaptation"):
  with members:
    - "optimal transport for domain adaptation"
    - "a plug play p300 bci using information geometry"
    - "domain adaptation for statistical classifiers"

Connected through hyperedge HE-4 (induced by source paper "systematic enhancement of functional connectivity in brain computer interfacing using common spatial patterns and tangent space mapping"):
  with members:
    - "a plug play p300 bci using information geometry"

Connected through hyperedge HE-5 (induced by source paper "a simple riemannian manifold network for image set classification"):
  with members:
    - "building deep networks on grassmann manifolds"
    - "wide residual networks"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "deep residual learning for image recognition"
    - "training deep networks with structured layers by matrix backpropagation"
    - "beyond gauss image set matching on the riemannian manifold of pdfs"
    - "pcanet a simple deep learning baseline for image classification"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (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"
    - "geodesic exponential kernels when curvature and linearity conflict"
    - "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-7 (induced by source paper "a universal framework for learning the elliptical mixture model"):
  with members:
    - "manopt a matlab toolbox for optimization on manifolds"
    - "convergence of the em algorithm for gaussian mixtures with unbalanced mixing coefficients"

Connected through hyperedge HE-8 (induced by source paper "manifold valued image generation with wasserstein generative adversarial nets"):
  with members:
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "spectral normalization for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "a geometric view of optimal transportation and generative model"
    - "improved training of wasserstein gans"
    - "learning from simulated and unsupervised images through adversarial training"
    - "energy based generative adversarial network"
    - "least squares generative adversarial networks"

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)