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 classification supervised auto encoder based on predefined evenly distributed class centroids"
  Abstract: Classic Autoencoders and variational autoencoders are used to learn complex data distributions, that are built on standard function approximators, such as neural networks, which can be trained by stochastic gradient descent methods. Especially, VAE has shown promise on a lot of complex task. In this paper, a new autoencoder model - classification supervised autoencoder (CSAE) based on predefined evenly-distributed class centroids (PEDCC) is proposed. To carry out the supervised learning for autoencoder, we use PEDCC of latent variables to train the network to ensure the maximization of inter-class distance and the minimization of inner-class distance. Instead of learning mean/variance of latent variables distribution and taking reparameterization of VAE, latent variables of CSAE are directly used to classify and as input of decoder. In addition, a new loss function is proposed to combine the loss function of classification, the loss function of image codec error and the loss function for enhancing subjective quality of decoded image. Based on the basic structure of the universal autoencoder, we realized the comprehensive optimal results of encoding, decoding and classification, and good model generalization performance at the same time. Theoretical advantages are reflected in experimental results.

Connected through hyperedge HE-1 (induced by source paper "semi supervised learning method based on predefined evenly distributed class centroids"):
  with members:
    - "a new loss function for cnn classifier based on pre defined evenly distributed class centroids"
    - "mixmatch a holistic approach to semi supervised learning"
    - "additive margin softmax for face verification"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "mixup beyond empirical risk minimization"
    - "regularization with stochastic transformations and perturbations for deep semi supervised learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "an image clustering auto encoder based on predefined evenly distributed class centroids and mmd distance"):
  with members:
    - "local deep feature alignment for unsupervised dimension reduction"
    - "cnn based joint clustering and representation learning with feature drift compensation for large scale image data"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "discriminatively boosted image clustering with fully convolutional auto encoders"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "joint unsupervised learning of deep representations and image clusters"

Connected through hyperedge HE-3 (induced by source paper "a new loss function for cnn classifier based on pre defined evenly distributed class centroids"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "emnist an extension of mnist to handwritten letters"
    - "squeeze and excitation networks"
    - "aggregated residual transformations for deep neural networks"
    - "deep residual learning for image recognition"

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)