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: "image generation from small datasets via batch statistics adaptation"
  Abstract: Thanks to the recent development of deep generative models, it is becoming possible to generate high-quality images with both fidelity and diversity. However, the training of such generative models requires a large dataset. To reduce the amount of data required, we propose a new method for transferring prior knowledge of the pre-trained generator, which is trained with a large dataset, to a small dataset in a different domain. Using such prior knowledge, the model can generate images leveraging some common sense that cannot be acquired from a small dataset. In this work, we propose a novel method focusing on the parameters for batch statistics, scale and shift, of the hidden layers in the generator. By training only these parameters in a supervised manner, we achieved stable training of the generator, and our method can generate higher quality images compared to previous methods without collapsing even when the dataset is small (~100). Our results show that the diversity of the filters acquired in the pre-trained generator is important for the performance on the target domain. By our method, it becomes possible to add a new class or domain to a pre-trained generator without disturbing the performance on the original domain.

Connected through hyperedge HE-1 (induced by source paper "on leveraging pretrained gans for limited data generation"):
  with members:
    - "infinite brain mr images pggan based data augmentation for tumor detection"
    - "transfer learning in biomedical natural language processing an evaluation of bert and elmo on ten benchmarking datasets"
    - "learning more with less conditional pggan based data augmentation for brain metastases detection using highly rough annotation on mr images"
    - "open event extraction from online text using a generative adversarial network"
    - "analyzing and improving the image quality of stylegan"
    - "generative adversarial network in medical imaging a review"
    - "a bert based transfer learning approach for hate speech detection in online social media"
    - "mind2mind transfer learning for gans"

Connected through hyperedge HE-2 (induced by source paper "freeze discriminator a simple baseline for fine tuning gans"):
  with members:
    - "learning what and where to transfer"
    - "consistency regularization for generative adversarial networks"
    - "analyzing and improving the image quality of stylegan"
    - "relational knowledge distillation"
    - "momentum contrast for unsupervised visual representation learning"
    - "few shot adversarial learning of realistic neural talking head models"
    - "stargan v2 diverse image synthesis for multiple domains"
    - "few shot unsupervised image to image translation"

Connected through hyperedge HE-3 (induced by source paper "minegan effective knowledge transfer from gans to target domains with few images"):
  with members:
    - "cgans with projection discriminator"
    - "how good is my gan"
    - "spectral normalization for generative adversarial networks"
    - "transferring gans generating images from limited data"
    - "deep generative models for distribution preserving lossy compression"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-4 (induced by source paper "meta reinforced synthetic data for one shot fine grained visual recognition"):
  with members:
    - "image deformation meta networks for one shot learning"
    - "how good is my gan"
    - "delta encoder an effective sample synthesis method for few shot object recognition"
    - "spectral normalization for generative adversarial networks"
    - "low shot learning from imaginary data"
    - "probabilistic model agnostic meta learning"
    - "modulating early visual processing by language"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-5 (induced by source paper "selective synthetic augmentation with quality assurance"):
  with members:
    - "generative image translation for data augmentation of bone lesion pathology"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "bagan data augmentation with balancing gan"
    - "transferring gans generating images from limited data"
    - "large scale gan training for high fidelity natural image synthesis"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "revisiting fine tuning for few shot learning"):
  with members:
    - "delta encoder an effective sample synthesis method for few shot object recognition"
    - "meta transfer learning for few shot learning"
    - "low shot learning from imaginary data"
    - "low shot learning with imprinted weights"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "matching networks for one shot learning"

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)