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: "deep quaternion networks"
  Abstract: The field of deep learning has seen significant advancement in recent years. However, much of the existing work has been focused on real-valued numbers. Recent work has shown that a deep learning system using the complex numbers can be deeper for a fixed parameter budget compared to its real-valued counterpart. In this work, we explore the benefits of generalizing one step further into the hyper-complex numbers, quaternions specifically, and provide the architecture components needed to build deep quaternion networks. We develop the theoretical basis by reviewing quaternion convolutions, developing a novel quaternion weight initialization scheme, and developing novel algorithms for quaternion batch-normalization. These pieces are tested in a classification model by end-to-end training on the CIFAR-10 and CIFAR-100 data sets and a segmentation model by end-to-end training on the KITTI Road Segmentation data set. These quaternion networks show improved convergence compared to real-valued and complex-valued networks, especially on the segmentation task, while having fewer parameters

Connected through hyperedge HE-1 (induced by source paper "modeling human motion with quaternion based neural networks"):
  with members:
    - "quaternion convolutional neural networks"
    - "a neural temporal model for human motion prediction"
    - "human motion modeling using dvgans"
    - "predicting future instance segmentations by forecasting convolutional features"
    - "quaternet a quaternion based recurrent model for human motion"
    - "convolutional sequence to sequence model for human dynamics"
    - "quaternion convolutional neural networks for end to end automatic speech recognition"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"

Connected through hyperedge HE-2 (induced by source paper "lightweight and efficient neural natural language processing with quaternion networks"):
  with members:
    - "quaternion collaborative filtering for recommendation"
    - "quaternion convolutional neural networks"
    - "universal transformers"
    - "quaternion knowledge graph embedding"
    - "attending to mathematical language with transformers"
    - "quaternet a quaternion based recurrent model for human motion"
    - "quaternion convolutional neural networks for end to end automatic speech recognition"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "3d rotation equivariant quaternion neural networks"):
  with members:
    - "complex valued neural networks for privacy protection"
    - "quaternion equivariant capsule networks for 3d point clouds"
    - "interpretable complex valued neural networks for privacy protection"
    - "quaternion convolutional neural networks"
    - "relation shape convolutional neural network for point cloud analysis"
    - "splatnet sparse lattice networks for point cloud processing"
    - "point2sequence learning the shape representation of 3d point clouds with an attention based sequence to sequence network"
    - "pointwise rotation invariant network with adaptive sampling and 3d spherical voxel convolution"

Connected through hyperedge HE-4 (induced by source paper "deep octonion networks"):
  with members:
    - "convolutional neural networks with alternately updated clique"
    - "on the origin of deep learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "an overview of multi task learning in deep neural networks"
    - "aggregated residual transformations for deep neural networks"
    - "fractalnet ultra deep neural networks without residuals"
    - "recent advances in convolutional neural networks"

Connected through hyperedge HE-5 (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"
    - "quaternion convolutional neural networks for heterogeneous image processing"
    - "knowledge graph embedding with entity neighbors and deep memory network"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "toruse knowledge graph embedding on a lie group"
    - "dynamic routing between capsules"
    - "complex and holographic embeddings of knowledge graphs a comparison"

Connected through hyperedge HE-6 (induced by source paper "compressing complex convolutional neural network based on an improved deep compression algorithm"):
  with members:
    - "efficient sparse winograd convolutional neural networks"
    - "deep complex networks"
    - "enabling sparse winograd convolution by native pruning"
    - "compact deep convolutional neural networks with coarse pruning"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "exploiting linear structure within convolutional networks for efficient evaluation"

Connected through hyperedge HE-7 (induced by source paper "quaternion convolutional neural networks"):
  with members:
    - "deep complex networks"
    - "mask r cnn"
    - "densely connected convolutional networks"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "neural networks on groups"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "universal approximations of permutation invariant equivariant functions by deep neural networks"
    - "on the universality of invariant networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "deep sets"
    - "neural ordinary differential equations"

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.NE(Neural and Evolutionary Computing)