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: "semi supervised learning with context conditional generative adversarial networks"
  Abstract: We introduce a simple semi-supervised learning approach for images based on in-painting using an adversarial loss. Images with random patches removed are presented to a generator whose task is to fill in the hole, based on the surrounding pixels. The in-painted images are then presented to a discriminator network that judges if they are real (unaltered training images) or not. This task acts as a regularizer for standard supervised training of the discriminator. Using our approach we are able to directly train large VGG-style networks in a semi-supervised fashion. We evaluate on STL-10 and PASCAL datasets, where our approach obtains performance comparable or superior to existing methods.

Connected through hyperedge HE-1 (induced by source paper "a survey towards federated semi supervised learning"):
  with members:
    - "federated learning with matched averaging"
    - "advances and open problems in federated learning"
    - "adaptive image feature learning for disease classification using inductive graph networks"
    - "unsupervised data augmentation for consistency training"
    - "federated machine learning concept and applications"
    - "mixmatch a holistic approach to semi supervised learning"
    - "federated adversarial domain adaptation"
    - "secureboost a lossless federated learning framework"

Connected through hyperedge HE-2 (induced by source paper "attribute guided sketch generation"):
  with members:
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "instagan instance aware image to image translation"
    - "r btn cross domain face composite and synthesis from limited facial patches"
    - "deformable gans for pose based human image generation"
    - "image generation from sketch constraint using contextual gan"
    - "unsupervised creation of parameterized avatars"
    - "unsupervised diverse colorization via generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "enaet self trained ensemble autoencoding transformations for semi supervised learning"):
  with members:
    - "small data challenges in big data era a survey of recent progress on unsupervised and semi supervised methods"
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "fast autoaugment"
    - "mixmatch a holistic approach to semi supervised learning"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "autoaugment learning augmentation policies from data"

Connected through hyperedge HE-4 (induced by source paper "coupled generative adversarial network for continuous fine grained action segmentation"):
  with members:
    - "stabilizing gan training with multiple random projections"
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "stabilizing training of generative adversarial networks through regularization"
    - "tricornet a hybrid temporal convolutional and recurrent network for video action segmentation"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "two stream lstm a deep fusion framework for human action recognition"
    - "seqgan sequence generative adversarial nets with policy gradient"

Connected through hyperedge HE-5 (induced by source paper "fine grained action segmentation using the semi supervised action gan"):
  with members:
    - "robust visual tracking using multi frame multi feature joint modeling"
    - "coupled generative adversarial network for continuous fine grained action segmentation"
    - "tracking by prediction a deep generative model for mutli person localisation and tracking"
    - "multi level sequence gan for group activity recognition"
    - "discrimnet semi supervised action recognition from videos using generative adversarial networks"
    - "good semi supervised learning that requires a bad gan"
    - "tricornet a hybrid temporal convolutional and recurrent network for video action segmentation"
    - "infogail interpretable imitation learning from visual demonstrations"

Connected through hyperedge HE-6 (induced by source paper "mixmatch a holistic approach to semi supervised learning"):
  with members:
    - "deep metric transfer for label propagation with limited annotated data"
    - "autoaugment learning augmentation policies from data"
    - "three mechanisms of weight decay regularization"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "invariant information clustering for unsupervised image classification and segmentation"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "decoupled weight decay regularization"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-7 (induced by source paper "memory augmented deep generative models for forecasting the next shot location in tennis"):
  with members:
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "deep decision trees for discriminative dictionary learning with adversarial multi agent trajectories"
    - "learning temporal strategic relationships using generative adversarial imitation learning"
    - "neural map structured memory for deep reinforcement learning"
    - "tree memory networks for modelling long term temporal dependencies"
    - "superposed episodic and semantic memory via sparse distributed representation"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "one shot imitation learning"

Connected through hyperedge HE-8 (induced by source paper "remixmatch semi supervised learning with distribution alignment and augmentation anchoring"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "unsupervised data augmentation for consistency training"
    - "mixmatch a holistic approach to semi supervised learning"
    - "deep metric transfer for label propagation with limited annotated data"
    - "autoaugment learning augmentation policies from data"
    - "unsupervised representation learning by predicting image rotations"
    - "three mechanisms of weight decay regularization"
    - "realistic evaluation of deep semi supervised learning algorithms"

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)