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: "quaternion convolutional neural networks for heterogeneous image processing"
  Abstract: Convolutional neural networks (CNN) have recently achieved state-of-the-art results in various applications. In the case of image recognition, an ideal model has to learn independently of the training data, both local dependencies between the three components (R,G,B) of a pixel, and the global relations describing edges or shapes, making it efficient with small or heterogeneous datasets. Quaternion-valued convolutional neural networks (QCNN) solved this problematic by introducing multidimensional algebra to CNN. This paper proposes to explore the fundamental reason of the success of QCNN over CNN, by investigating the impact of the Hamilton product on a color image reconstruction task performed from a gray-scale only training. By learning independently both internal and external relations and with less parameters than real valued convolutional encoder-decoder (CAE), quaternion convolutional encoder-decoders (QCAE) perfectly reconstructed unseen color images while CAE produced worst and gray-scale versions.

Connected through hyperedge HE-1 (induced by source paper "hypercomplex valued recurrent correlation neural networks"):
  with members:
    - "an introduction to quaternion valued recurrent projection neural networks"
    - "dense associative memory for pattern recognition"

Connected through hyperedge HE-2 (induced by source paper "quaternion product units for deep learning on 3d rotation groups"):
  with members:
    - "a neural network based on spd manifold learning for skeleton based hand gesture recognition"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "quaternion convolutional neural networks"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "quaternet a quaternion based recurrent model for human motion"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"
    - "3d steerable cnns learning rotationally equivariant features in volumetric data"
    - "voxelnet end to end learning for point cloud based 3d object detection"

Connected through hyperedge HE-3 (induced by source paper "quaternion knowledge graph embedding"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "quaternion convolutional neural networks for end to end automatic speech recognition"
    - "knowledge graph embedding with entity neighbors and deep memory network"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "deep quaternion networks"
    - "toruse knowledge graph embedding on a lie group"
    - "dynamic routing between capsules"
    - "complex and holographic embeddings of knowledge graphs a comparison"

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)