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: "manifoldnet a deep network framework for manifold valued data"
  Abstract: Deep neural networks have become the main work horse for many tasks involving learning from data in a variety of applications in Science and Engineering. Traditionally, the input to these networks lie in a vector space and the operations employed within the network are well defined on vector-spaces. In the recent past, due to technological advances in sensing, it has become possible to acquire manifold-valued data sets either directly or indirectly. Examples include but are not limited to data from omnidirectional cameras on automobiles, drones etc., synthetic aperture radar imaging, diffusion magnetic resonance imaging, elastography and conductance imaging in the Medical Imaging domain and others. Thus, there is need to generalize the deep neural networks to cope with input data that reside on curved manifolds where vector space operations are not naturally admissible. In this paper, we present a novel theoretical framework to generalize the widely popular convolutional neural networks (CNNs) to high dimensional manifold-valued data inputs. We call these networks, ManifoldNets. #R##N#In ManifoldNets, convolution operation on data residing on Riemannian manifolds is achieved via a provably convergent recursive computation of the weighted Fr\'{e}chet Mean (wFM) of the given data, where the weights makeup the convolution mask, to be learned. Further, we prove that the proposed wFM layer achieves a contraction mapping and hence ManifoldNet does not need the non-linear ReLU unit used in standard CNNs. We present experiments, using the ManifoldNet framework, to achieve dimensionality reduction by computing the principal linear subspaces that naturally reside on a Grassmannian. The experimental results demonstrate the efficacy of ManifoldNets in the context of classification and reconstruction accuracy.

Connected through hyperedge HE-1 (induced by source paper "surreal complex valued deep learning as principled transformations on a rotational lie group"):
  with members:
    - "a statistical recurrent model on the manifold of symmetric positive definite matrices"
    - "h cnns convolutional neural networks for riemannian homogeneous spaces"
    - "polar transformer networks"
    - "deep complex networks"
    - "geometric deep learning going beyond euclidean data"
    - "convolutional networks for spherical signals"
    - "joint embedding and classification for sar target recognition"
    - "harmonic networks deep translation and rotation equivariance"

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"
    - "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"
    - "second order convolutional neural networks"
    - "building deep networks on grassmann manifolds"

Connected through hyperedge HE-3 (induced by source paper "riemannian batch normalization for spd neural networks"):
  with members:
    - "a statistical recurrent model on the manifold of symmetric positive definite matrices"
    - "learning a robust representation via a deep network on symmetric positive definite manifolds"
    - "when kernel methods meet feature learning log covariance network for action recognition from skeletal data"
    - "deepkspd learning kernel matrix based spd representation for fine grained image recognition"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "building deep networks on grassmann manifolds"
    - "deep learning on lie groups for skeleton based action recognition"
    - "a riemannian network for spd matrix learning"

Connected through hyperedge HE-4 (induced by source paper "surreal fr echet mean and distance transform for complex valued deep learning"):
  with members:
    - "h cnns convolutional neural networks for riemannian homogeneous spaces"
    - "polar transformer networks"
    - "deep complex networks"
    - "joint embedding and classification for sar target recognition"
    - "harmonic networks deep translation and rotation equivariance"
    - "convolutional radio modulation recognition networks"
    - "group equivariant convolutional networks"
    - "affinity cnn learning pixel centric pairwise relations for figure ground embedding"

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)