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: "and autoregressive novelty detectors"
  Abstract: We propose an unsupervised model for novelty detection. The subject is treated as a density estimation problem, in which a deep neural network is employed to learn a parametric function that maximizes probabilities of training samples. This is achieved by equipping an autoencoder with a novel module, responsible for the maximization of compressed codes' likelihood by means of autoregression. We illustrate design choices and proper layers to perform autoregressive density estimation when dealing with both image and video inputs. Despite a very general formulation, our model shows promising results in diverse one-class novelty detection and video anomaly detection benchmarks.

Connected through hyperedge HE-1 (induced by source paper "deep cnn based multi task learning for open set recognition"):
  with members:
    - "learning deep features for one class classification"
    - "open category detection with pac guarantees"
    - "deep anomaly detection using geometric transformations"
    - "deep anomaly detection with outlier exposure"
    - "learning from noisy web data with category level supervision"
    - "classification reconstruction learning for open set recognition"
    - "the extreme value machine"
    - "enhancing the reliability of out of distribution image detection in neural networks"

Connected through hyperedge HE-2 (induced by source paper "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection"):
  with members:
    - "mptv matching pursuit based total variation minimization for image deconvolution"
    - "memorization precedes generation learning unsupervised gans with memory networks"
    - "adversarially learned one class classifier for novelty detection"
    - "deep anomaly detection using geometric transformations"
    - "anomaly detection using one class neural networks"
    - "unmasking the abnormal events in video"
    - "joint detection and recounting of abnormal events by learning deep generic knowledge"
    - "abnormal event detection in videos using spatiotemporal autoencoder"

Connected through hyperedge HE-3 (induced by source paper "learning deep features for one class classification"):
  with members:
    - "one class convolutional neural network"
    - "active authentication using an autoencoder regularized cnn based one class classifier"
    - "deep transfer learning for multiple class novelty detection"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "latent space autoregression for novelty detection"
    - "anomaly detection using one class neural networks"
    - "sparse representation based open set recognition"
    - "active user authentication for smartphones a challenge data set and benchmark results"

Connected through hyperedge HE-4 (induced by source paper "one class convolutional neural network"):
  with members:
    - "learning deep features for one class classification"
    - "adversarially learned one class classifier for novelty detection"
    - "anomaly detection using one class neural networks"
    - "robust deep and inductive anomaly detection"
    - "abnormal event detection in videos using generative adversarial nets"
    - "active user authentication for smartphones a challenge data set and benchmark results"
    - "improved techniques for training gans"
    - "a learned representation for artistic style"

Connected through hyperedge HE-5 (induced by source paper "ocgan one class novelty detection using gans with constrained latent representations"):
  with members:
    - "one class convolutional neural network"
    - "active authentication using an autoencoder regularized cnn based one class classifier"
    - "deep transfer learning for multiple class novelty detection"
    - "learning deep features for one class classification"
    - "adversarially learned one class classifier for novelty detection"
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "conditional image generation with pixelcnn decoders"

Connected through hyperedge HE-6 (induced by source paper "unsupervised anomaly localization using variational auto encoders"):
  with members:
    - "diagnosing and enhancing vae models"
    - "unsupervised detection of lesions in brain mri using constrained adversarial auto encoders"
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "deep generative models in the real world an open challenge from medical imaging"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

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)