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: "beyond gauss image set matching on the riemannian manifold of pdfs"
  Abstract: State-of-the-art image-set matching techniques typically implicitly model each image-set with a Gaussian distribution. Here, we propose to go beyond these representations and model image-sets as probability distribution functions (PDFs) using kernel density estimators. To compare and match image-sets, we exploit Csiszar f-divergences, which bear strong connections to the geodesic distance defined on the space of PDFs, i.e., the statistical manifold. Furthermore, we introduce valid positive definite kernels on the statistical manifolds, which let us make use of more powerful classification schemes to match image-sets. Finally, we introduce a supervised dimensionality reduction technique that learns a latent space where f-divergences reflect the class labels of the data. Our experiments on diverse problems, such as video-based face recognition and dynamic texture classification, evidence the benefits of our approach over the state-of-the-art image-set matching methods.

Connected through hyperedge HE-1 (induced by source paper "a deeper look at power normalizations"):
  with members:
    - "improved bilinear pooling with cnns"
    - "domain adaptation by mixture of alignments of second or higher order scatter tensors"
    - "a riemannian network for spd matrix learning"
    - "bilinear cnn models for fine grained visual recognition"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "deep residual learning for image recognition"
    - "places205 vggnet models for scene recognition"

Connected through hyperedge HE-2 (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"
    - "pcanet a simple deep learning baseline for image classification"
    - "very deep convolutional networks for large scale image recognition"
    - "kernel methods on the riemannian manifold of symmetric positive definite matrices"

Connected through hyperedge HE-3 (induced by source paper "manifold regularized slow feature analysis for dynamic texture recognition"):
  with members:
    - "slow feature analysis for human action recognition"
    - "dynamic texture and scene classification by transferring deep image features"
    - "sa cnn dynamic scene classification using convolutional neural networks"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "deep image set hashing"):
  with members:
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "learning to hash for indexing big data a survey"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "learning face representation from scratch"

Connected through hyperedge HE-5 (induced by source paper "representation learning with deep extreme learning machines for efficient image set classification"):
  with members:
    - "hsr l1 2 regularized sparse representation for fast face recognition using hierarchical feature selection"
    - "from manifold to manifold geometry aware dimensionality reduction for spd matrices"
    - "representation learning a review and new perspectives"

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)