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: "aga attribute guided augmentation"
  Abstract: We consider the problem of data augmentation, i.e., generating artificial samples to extend a given corpus of training data. Specifically, we propose attributed-guided augmentation (AGA) which learns a mapping that allows to synthesize data such that an attribute of a synthesized sample is at a desired value or strength. This is particularly interesting in situations where little data with no attribute annotation is available for learning, but we have access to a large external corpus of heavily annotated samples. While prior works primarily augment in the space of images, we propose to perform augmentation in feature space instead. We implement our approach as a deep encoder-decoder architecture that learns the synthesis function in an end-to-end manner. We demonstrate the utility of our approach on the problems of (1) one-shot object recognition in a transfer-learning setting where we have no prior knowledge of the new classes, as well as (2) object-based one-shot scene recognition. As external data, we leverage 3D depth and pose information from the SUN RGB-D dataset. Our experiments show that attribute-guided augmentation of high-level CNN features considerably improves one-shot recognition performance on both problems.

Connected through hyperedge HE-1 (induced by source paper "multi level semantic feature augmentation for one shot learning"):
  with members:
    - "deep meta learning learning to learn in the concept space"
    - "low shot learning from imaginary data"
    - "memory matching networks for one shot image recognition"
    - "dynamic few shot visual learning without forgetting"
    - "prototypical networks for few shot learning"
    - "few shot image recognition by predicting parameters from activations"
    - "meta sgd learning to learn quickly for few shot learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "few shot unsupervised image to image translation"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "on first order meta learning algorithms"
    - "multimodal unsupervised image to image translation"
    - "one shot unsupervised cross domain translation"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-3 (induced by source paper "saliency for fine grained object recognition in domains with scarce training data"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "deep visual attention prediction"
    - "feature space transfer for data augmentation"
    - "dynamic few shot visual learning without forgetting"
    - "deep layer aggregation"
    - "prototypical networks for few shot learning"
    - "exploiting inter image similarity and ensemble of extreme learners for fixation prediction using deep features"
    - "fine grained categorization via cnn based automatic extraction and integration of object level and part level features"

Connected through hyperedge HE-4 (induced by source paper "thinking outside the pool active training image creation for relative attributes"):
  with members:
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "generative adversarial active learning"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "active decision boundary annotation with deep generative models"
    - "adversarial transformation networks learning to generate adversarial examples"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning from synthetic humans"

Connected through hyperedge HE-5 (induced by source paper "diversity transfer network for few shot learning"):
  with members:
    - "few shot learning via saliency guided hallucination of samples"
    - "image deformation meta networks for one shot learning"
    - "meta learning with latent embedding optimization"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "delta encoder an effective sample synthesis method for few shot object recognition"
    - "feature space transfer for data augmentation"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "low shot learning from imaginary data"

Connected through hyperedge HE-6 (induced by source paper "attributes as operators factorizing unseen attribute object compositions"):
  with members:
    - "neural algebra of classifiers"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "joint discovery of object states and manipulation actions"
    - "alternative semantic representations for zero shot human action recognition"
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "feature generating networks for zero shot learning"
    - "zero shot learning the good the bad and the ugly"

Connected through hyperedge HE-7 (induced by source paper "sequential attention gan for interactive image editing via dialogue"):
  with members:
    - "learning to globally edit images with textual description"
    - "text adaptive generative adversarial networks manipulating images with natural language"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"
    - "neural approaches to conversational ai"
    - "the neural painter multi turn image generation"
    - "dialog based interactive image retrieval"
    - "storygan a sequential conditional gan for story visualization"
    - "image grounded conversations multimodal context for natural question and response generation"

Connected through hyperedge HE-8 (induced by source paper "feature transfer learning for deep face recognition with under represented data"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "multi task convolutional neural network for pose invariant face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "normface l 2 hypersphere embedding for face verification"
    - "the devil is in the tails fine grained classification in the wild"
    - "sphereface deep hypersphere embedding for face recognition"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "reconstruction based disentanglement for pose invariant face 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)