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: "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
  Abstract: Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging. Models are typically based on large amounts of data with annotated examples of known markers aiming at automating detection. High annotation effort and the limitation to a vocabulary of known markers limit the power of such approaches. Here, we perform unsupervised learning to identify anomalies in imaging data as candidates for markers. We propose AnoGAN, a deep convolutional generative adversarial network to learn a manifold of normal anatomical variability, accompanying a novel anomaly scoring scheme based on the mapping from image space to a latent space. Applied to new data, the model labels anomalies, and scores image patches indicating their fit into the learned distribution. Results on optical coherence tomography images of the retina demonstrate that the approach correctly identifies anomalous images, such as images containing retinal fluid or hyperreflective foci.

Connected through hyperedge HE-1 (induced by source paper "compact surjective encoding autoencoder for unsupervised novelty detection"):
  with members:
    - "deep semi supervised anomaly detection"
    - "one class convolutional neural network"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "adversarially learned one class classifier for novelty detection"
    - "deep anomaly detection using geometric transformations"
    - "latent space autoregression for novelty detection"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "arae adversarially robust training of autoencoders improves novelty detection"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "deep learning for anomaly detection a survey"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection"
    - "rare event detection using disentangled representation learning"
    - "real world anomaly detection in surveillance videos"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "adversarially learned one class classifier for novelty detection"

Connected through hyperedge HE-3 (induced by source paper "infinite brain mr images pggan based data augmentation for tumor detection"):
  with members:
    - "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"
    - "brain tumor segmentation with deep neural networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "how to fool radiologists with generative adversarial networks a visual turing test for lung cancer diagnosis"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-4 (induced by source paper "simple and effective prevention of mode collapse in deep one class classification"):
  with members:
    - "deep semi supervised anomaly detection"
    - "robust anomaly detection in images using adversarial autoencoders"
    - "efficient gan based anomaly detection"
    - "deep anomaly detection using geometric transformations"
    - "escaping from collapsing modes in a constrained space"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "began boundary equilibrium generative adversarial networks"
    - "noisy softmax improving the generalization ability of dcnn via postponing the early softmax saturation"

Connected through hyperedge HE-5 (induced by source paper "memory augmented generative adversarial networks for anomaly detection"):
  with members:
    - "regularized cycle consistent generative adversarial network for anomaly detection"
    - "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection"
    - "memorization precedes generation learning unsupervised gans with memory networks"
    - "anomaly detection with multiple hypotheses predictions"
    - "adversarially learned anomaly detection"
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "enhancing the reliability of out of distribution image detection in neural networks"

Connected through hyperedge HE-6 (induced by source paper "conditional gaussian distribution learning for open set recognition"):
  with members:
    - "mnemonics training multi class incremental learning without forgetting"
    - "c2ae class conditioned auto encoder for open set recognition"
    - "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning"
    - "doc deep open classification of text documents"
    - "sparse representation based open set recognition"
    - "generative openmax for multi class open set classification"
    - "mask r cnn"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-7 (induced by source paper "advae a self adversarial variational autoencoder with gaussian anomaly prior knowledge for anomaly detection"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "pyod a python toolbox for scalable outlier detection"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "are generative deep models for novelty detection truly better"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "generative adversarial active learning for unsupervised outlier detection"

Connected through hyperedge HE-8 (induced by source paper "deep residual flow for novelty detection"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "safer classification by synthesis"
    - "density estimation using real nvp"
    - "concrete problems in ai safety"
    - "universal adversarial perturbations"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"
    - "deep residual learning for image 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)