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: "a statistical recurrent model on the manifold of symmetric positive definite matrices"
  Abstract: In a number of disciplines, the data (e.g., graphs, manifolds) to be analyzed are non-Euclidean in nature. Geometric deep learning corresponds to techniques that generalize deep neural network models to such non-Euclidean spaces. Several recent papers have shown how convolutional neural networks (CNNs) can be extended to learn with graph-based data. In this work, we study the setting where the data (or measurements) are ordered, longitudinal or temporal in nature and live on a Riemannian manifold -- this setting is common in a variety of problems in statistical machine learning, vision and medical imaging. We show how recurrent statistical recurrent network models can be defined in such spaces. We give an efficient algorithm and conduct a rigorous analysis of its statistical properties. We perform extensive numerical experiments demonstrating competitive performance with state of the art methods but with significantly less number of parameters. We also show applications to a statistical analysis task in brain imaging, a regime where deep neural network models have only been utilized in limited ways.

Connected through hyperedge HE-1 (induced by source paper "cpr gcn conditional partial residual graph convolutional network in automated anatomical labeling of coronary arteries"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "residual gated graph convnets"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "neural ordinary differential equations"
    - "gated graph sequence neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "deep convolutional networks on graph structured data"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "generating high fidelity synthetic time series datasets with doppelganger"):
  with members:
    - "infogan cr disentangling generative adversarial networks with contrastive regularizers"
    - "differentially private generative adversarial networks for time series continuous and discrete open data"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "differentially private generative adversarial network"
    - "progressive growing of gans for improved quality stability and variation"
    - "generating multi label discrete patient records using generative adversarial networks"
    - "gated orthogonal recurrent units on learning to forget"

Connected through hyperedge HE-3 (induced by source paper "surreal complex valued deep learning as principled transformations on a rotational lie group"):
  with members:
    - "manifoldnet a deep network framework for manifold valued data"
    - "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-4 (induced by source paper "dilated convolutional neural networks for sequential manifold valued data"):
  with members:
    - "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"
    - "second order convolutional neural networks"
    - "building deep networks on grassmann manifolds"

Connected through hyperedge HE-5 (induced by source paper "riemannian batch normalization for spd neural networks"):
  with members:
    - "manifoldnet a deep network framework for manifold valued data"
    - "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"

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)