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: "learning from synthetic data using a stacked multichannel autoencoder"
  Abstract: Learning from synthetic data has many important and practical applications. An example of application is photo-sketch recognition. Using synthetic data is challenging due to the differences in feature distributions between synthetic and real data, a phenomenon we term synthetic gap. In this paper, we investigate and formalize a general framework-Stacked Multichannel Autoencoder (SMCAE) that enables bridging the synthetic gap and learning from synthetic data more efficiently. In particular, we show that our SMCAE can not only transform and use synthetic data on the challenging face-sketch recognition task, but that it can also help simulate real images, which can be used for training classifiers for recognition. Preliminary experiments validate the effectiveness of the framework.

Connected through hyperedge HE-1 (induced by source paper "towards robust audio spoofing detection a detailed comparison of traditional and learned features"):
  with members:
    - "singing voice separation using a deep convolutional neural network trained by ideal binary mask and cross entropy"
    - "a study on replay attack and anti spoofing for automatic speaker verification"
    - "utd crss systems for 2016 nist speaker recognition evaluation"
    - "voice recognition algorithms using mel frequency cepstral coefficient mfcc and dynamic time warping dtw techniques"

Connected through hyperedge HE-2 (induced by source paper "supervised cosmos autoencoder learning beyond the euclidean loss"):
  with members:
    - "residual codean autoencoder for facial attribute analysis"
    - "semantic autoencoder for zero shot learning"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "relational autoencoder for feature extraction"
    - "improving facial attribute prediction using semantic segmentation"
    - "dynamic routing between capsules"
    - "age progression regression by conditional adversarial autoencoder"
    - "a good practice towards top performance of face recognition transferred deep feature fusion"

Connected through hyperedge HE-3 (induced by source paper "algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning"):
  with members:
    - "remote sensing image scene classification benchmark and state of the art"
    - "aggregated residual transformations for deep neural networks"
    - "identity mappings in deep residual networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "synthesizing training images for boosting human 3d pose estimation"
    - "spectral angle based unary energy functions for spatial spectral hyperspectral classification using markov random fields"
    - "learning to segment object candidates"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "generating image sequence from description with lstm conditional gan"):
  with members:
    - "cgmos certainty guided minority oversampling"
    - "generative adversarial text to image synthesis"
    - "image to image translation with conditional adversarial networks"
    - "context encoders feature learning by inpainting"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "skip thought vectors"
    - "generating images from captions with attention"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "are you eligible predicting adulthood from face images via class specific mean autoencoder"):
  with members:
    - "unravelling robustness of deep learning based face recognition against adversarial attacks"
    - "relational autoencoder for feature extraction"
    - "multi view face detection using deep convolutional neural networks"
    - "facenet a unified embedding for face recognition and clustering"
    - "naive deep face recognition touching the limit of lfw benchmark or not"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (induced by source paper "segment based credit scoring using latent clusters in the variational autoencoder"):
  with members:
    - "deep unsupervised clustering using mixture of autoencoders"
    - "deep feature consistent variational autoencoder"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "generating sentences from a continuous space"
    - "issues challenges and tools of clustering algorithms"

Connected through hyperedge HE-7 (induced by source paper "efficient convolutional network learning using parametric log based dual tree wavelet scatternet"):
  with members:
    - "dual tree wavelet scattering network with parametric log transformation for object classification"
    - "visualizing and improving scattering networks"
    - "wide residual networks"
    - "multi resolution dual tree wavelet scattering network for signal classification"
    - "very deep convolutional networks for large scale image recognition"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "deep roto translation scattering for object classification"
    - "deep epitomic convolutional 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.CV(Computer Vision and Pattern Recognition)