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: "second order convolutional neural networks"
  Abstract: Convolutional Neural Networks (CNNs) have been successfully applied to many computer vision tasks, such as image classification. By performing linear combinations and element-wise nonlinear operations, these networks can be thought of as extracting solely first-order information from an input image. In the past, however, second-order statistics computed from handcrafted features, e.g., covariances, have proven highly effective in diverse recognition tasks. In this paper, we introduce a novel class of CNNs that exploit second-order statistics. To this end, we design a series of new layers that (i) extract a covariance matrix from convolutional activations, (ii) compute a parametric, second-order transformation of a matrix, and (iii) perform a parametric vectorization of a matrix. These operations can be assembled to form a Covariance Descriptor Unit (CDU), which replaces the fully-connected layers of standard CNNs. Our experiments demonstrate the benefits of our new architecture, which outperform the first-order CNNs, while relying on up to 90% fewer parameters.

Connected through hyperedge HE-1 (induced by source paper "mvc net a convolutional neural network architecture for manifold valued images with applications"):
  with members:
    - "riemannian batch normalization for spd neural networks"
    - "multi directional geodesic neural networks via equivariant convolution"
    - "learning so 3 equivariant representations with spherical cnns"
    - "geometric deep learning going beyond euclidean data"
    - "building deep networks on grassmann manifolds"
    - "a riemannian network for spd matrix learning"
    - "geodesic convolutional neural networks on riemannian manifolds"

Connected through hyperedge HE-2 (induced by source paper "dilated convolutional neural networks for sequential manifold valued data"):
  with members:
    - "a statistical recurrent model on the manifold of symmetric positive definite matrices"
    - "manifoldnet a deep network framework for manifold valued data"
    - "h cnns convolutional neural networks for riemannian homogeneous spaces"
    - "covariant compositional networks for learning graphs"
    - "the statistical recurrent unit"
    - "tensor train recurrent neural networks for video classification"
    - "geometric deep learning going beyond euclidean data"
    - "building deep networks on grassmann manifolds"

Connected through hyperedge HE-3 (induced by source paper "second order democratic aggregation"):
  with members:
    - "a deeper look at power normalizations"
    - "statistically motivated second order pooling"
    - "is second order information helpful for large scale visual recognition"
    - "improved bilinear pooling with cnns"
    - "tensor representations via kernel linearization for action recognition from 3d skeletons"
    - "interferences in match kernels"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"

Connected through hyperedge HE-4 (induced by source paper "covariance pooling for facial expression recognition"):
  with members:
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "learning grimaces by watching tv"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "a riemannian network for spd matrix learning"

Connected through hyperedge HE-5 (induced by source paper "second order temporal pooling for action recognition"):
  with members:
    - "moments in time dataset one million videos for event understanding"
    - "non linear temporal subspace representations for activity recognition"
    - "video representation learning using discriminative pooling"
    - "higher order pooling of cnn features via kernel linearization for action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "generalized rank pooling for activity recognition"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-6 (induced by source paper "learning a robust representation via a deep network on symmetric positive definite manifolds"):
  with members:
    - "deep manifold to manifold transforming network for action recognition"
    - "deepkspd learning kernel matrix based spd representation for fine grained image recognition"
    - "is second order information helpful for large scale visual recognition"
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"
    - "exploiting structure sparsity for covariance based visual representation"
    - "deep ten texture encoding network"
    - "learning an invariant hilbert space for domain adaptation"
    - "geometry aware similarity learning on spd manifolds for visual recognition"

Connected through hyperedge HE-7 (induced by source paper "hierarchical gaussian descriptors with application to person re identification"):
  with members:
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"
    - "learning a discriminative null space for person re identification"
    - "a comprehensive evaluation and benchmark for person re identification features metrics and datasets"
    - "person re identification past present and future"

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)