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: "on complex valued convolutional neural networks"
  Abstract: Convolutional neural networks (CNNs) are the cutting edge model for supervised machine learning in computer vision. In recent years CNNs have outperformed traditional approaches in many computer vision tasks such as object detection, image classification and face recognition. CNNs are vulnerable to overfitting, and a lot of research focuses on finding regularization methods to overcome it. One approach is designing task specific models based on prior knowledge. #R##N#Several works have shown that properties of natural images can be easily captured using complex numbers. Motivated by these works, we present a variation of the CNN model with complex valued input and weights. We construct the complex model as a generalization of the real model. Lack of order over the complex field raises several difficulties both in the definition and in the training of the network. We address these issues and suggest possible solutions. #R##N#The resulting model is shown to be a restricted form of a real valued CNN with twice the parameters. It is sensitive to phase structure, and we suggest it serves as a regularized model for problems where such structure is important. This suggestion is verified empirically by comparing the performance of a complex and a real network in the problem of cell detection. The two networks achieve comparable results, and although the complex model is hard to train, it is significantly less vulnerable to overfitting. We also demonstrate that the complex network detects meaningful phase structure in the data.

Connected through hyperedge HE-1 (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-2 (induced by source paper "deep complex networks for protocol agnostic radio frequency device fingerprinting in the wild"):
  with members:
    - "deepradioid real time channel resilient optimization of deep learning based radio fingerprinting algorithms"
    - "gated complex recurrent neural networks"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "complex gated recurrent neural networks"
    - "an introduction to deep learning for the physical layer"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "deep complex networks"
    - "image augmentation using radial transform for training deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "lending orientation to neural networks for cross view geo localization"):
  with members:
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "deep stochastic attraction and repulsion embedding for image based localization"
    - "cross view image synthesis using conditional gans"
    - "cross view image matching for geo localization in urban environments"
    - "deep complex networks"
    - "deep metric learning with angular loss"
    - "localizing and orienting street views using overhead imagery"
    - "densely connected convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "implementation of optical deep neural networks using the fabry perot interferometer"):
  with members:
    - "large scale optical neural networks based on photoelectric multiplication"
    - "reinforcement learning in a large scale photonic recurrent neural network"
    - "kronecker recurrent units"
    - "gated orthogonal recurrent units on learning to forget"
    - "the numerics of gans"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "searching for activation functions"
    - "associative long short term memory"

Connected through hyperedge HE-5 (induced by source paper "analyzing knowledge graph embedding methods from a multi embedding interaction perspective"):
  with members:
    - "on multi relational link prediction with bilinear models"
    - "translation based recommendation"
    - "convolutional 2d knowledge graph embeddings"
    - "complex embeddings for simple link prediction"
    - "modeling relation paths for representation learning of knowledge bases"
    - "transa an adaptive approach for knowledge graph embedding"
    - "adam a method for stochastic optimization"
    - "embedding entities and relations for learning and inference in knowledge bases"

Connected through hyperedge HE-6 (induced by source paper "widely linear kernels for complex valued kernel activation functions"):
  with members:
    - "complex evolution recurrent neural networks cernns"
    - "complex gaussian processes for regression"
    - "deep complex networks"
    - "emnist an extension of mnist to handwritten letters"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "complex support vector machines for regression and quaternary classification"
    - "unitary evolution recurrent neural networks"
    - "extension of wirtinger s calculus to reproducing kernel hilbert spaces and the complex kernel lms"

Connected through hyperedge HE-7 (induced by source paper "resonant machine learning based on complex growth transform dynamical systems"):
  with members:
    - "deep quaternion networks"
    - "deep complex networks"
    - "a mathematical motivation for complex valued convolutional networks"
    - "complex support vector machines for regression and quaternary classification"

Connected through hyperedge HE-8 (induced by source paper "complex valued neural networks with non parametric activation functions"):
  with members:
    - "self normalizing neural networks"
    - "better than real complex valued neural nets for mri fingerprinting"
    - "deep complex networks"
    - "swish a self gated activation function"
    - "associative long short term memory"
    - "unitary evolution recurrent neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "learning activation functions to improve deep neural networks"

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)