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: "joint embedding and classification for sar target recognition"
  Abstract: Deep learning can be an effective and efficient means to automatically detect and classify targets in synthetic aperture radar (SAR) images, but it is critical for trained neural networks to be robust to variations that exist between training and test environments. The layers in a neural network can be understood as successive transformations of an input image into embedded feature representations and ultimately into a semantic class label. To address the overfitting problem in SAR target classification, we train neural networks to optimize the spatial clustering of points in the embedded space in addition to optimizing the final classification score. We demonstrate that networks trained with this dual embedding and classification loss outperform networks with classification loss only. We study placing the embedding loss after different network layers and find that applying the embedding loss on the classification space results in the best SAR classification performance. Finally, our visualization of the network's ten-dimensional classification space supports our claim that the embedding loss encourages greater separation between target class clusters for both training and testing partitions of the MSTAR dataset.

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"
    - "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"
    - "harmonic networks deep translation and rotation equivariance"

Connected through hyperedge HE-2 (induced by source paper "surreal fr echet mean and distance transform for complex valued deep learning"):
  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"
    - "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)