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: "introspective classification with convolutional nets"
  Abstract: We propose introspective convolutional networks (ICN) that emphasize the importance of having convolutional neural networks empowered with generative capabilities. We employ a reclassification-by-synthesis algorithm to perform training using a formulation stemmed from the Bayes theory. Our ICN tries to iteratively: (1) synthesize pseudo-negative samples; and (2) enhance itself by improving the classification. The single CNN classifier learned is at the same time generative --- being able to directly synthesize new samples within its own discriminative model. We conduct experiments on benchmark datasets including MNIST, CIFAR-10, and SVHN using state-of-the-art CNN architectures, and observe improved classification results.

Connected through hyperedge HE-1 (induced by source paper "reusing discriminators for encoding towards unsupervised image to image translation"):
  with members:
    - "transgaga geometry aware unsupervised image to image translation"
    - "travelgan image to image translation by transformation vector learning"
    - "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "patch based image inpainting with generative adversarial networks"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "drit diverse image to image translation via disentangled representations"

Connected through hyperedge HE-2 (induced by source paper "learning to generate synthetic data via compositing"):
  with members:
    - "unsupervised 3d pose estimation with geometric self supervision"
    - "spatially transformed adversarial examples"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "loss rank mining a general hard example mining method for real time detectors"
    - "modeling visual context is key to augmenting object detection datasets"
    - "unsupervised hard example mining from videos for improved object detection"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "can 3d pose be learned from 2d projections alone"

Connected through hyperedge HE-3 (induced by source paper "learning energy based spatial temporal generative convnets for dynamic patterns"):
  with members:
    - "videoflow a flow based generative model for video"
    - "learning descriptor networks for 3d shape synthesis and analysis"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "wasserstein introspective neural networks"
    - "two stream convolutional networks for dynamic texture synthesis"
    - "the kinetics human action video dataset"
    - "mocogan decomposing motion and content for video generation"
    - "synthesising dynamic textures using convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "a novel unsupervised post processing calibration method for dnns with robustness to domain shift"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "end to end learning for self driving cars"
    - "overcoming catastrophic forgetting in neural networks"
    - "wide residual networks"

Connected through hyperedge HE-5 (induced by source paper "normalizing flows for deep anomaly detection"):
  with members:
    - "normalizing flows for novelty detection in industrial time series data"
    - "anomaly detection using one class neural networks"
    - "xgboost a scalable tree boosting system"
    - "improving variational inference with inverse autoregressive flow"
    - "density estimation using real nvp"

Connected through hyperedge HE-6 (induced by source paper "deep co training for semi supervised image recognition"):
  with members:
    - "multi scale spatially asymmetric recalibration for image classification"
    - "good semi supervised learning that requires a bad gan"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "few shot image recognition by predicting parameters from activations"
    - "sort second order response transform for visual recognition"
    - "gradually updated neural networks for large scale image recognition"
    - "single shot object detection with enriched semantics"
    - "adversarial examples for semantic segmentation and object detection"

Connected through hyperedge HE-7 (induced by source paper "towards resisting large data variations via introspective learning"):
  with members:
    - "wasserstein introspective neural networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "improved techniques for training gans"
    - "matching networks for one shot learning"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "attended temperature scaling a practical approach for calibrating deep neural networks"):
  with members:
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "end to end learning for self driving cars"
    - "skin lesion analysis toward melanoma detection a challenge at the 2017 international symposium on biomedical imaging isbi hosted by the international skin imaging collaboration isic"
    - "wide residual networks"
    - "bayesian dark knowledge"

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)