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: "group anomaly detection using deep generative models"
  Abstract: Unlike conventional anomaly detection research that focuses on point anomalies, our goal is to detect anomalous collections of individual data points. In particular, we perform group anomaly detection (GAD) with an emphasis on irregular group distributions (e.g. irregular mixtures of image pixels). GAD is an important task in detecting unusual and anomalous phenomena in real-world applications such as high energy particle physics, social media, and medical imaging. In this paper, we take a generative approach by proposing deep generative models: Adversarial autoencoder (AAE) and variational autoencoder (VAE) for group anomaly detection. Both AAE and VAE detect group anomalies using point-wise input data where group memberships are known a priori. We conduct extensive experiments to evaluate our models on real-world datasets. The empirical results demonstrate that our approach is effective and robust in detecting group anomalies.

Connected through hyperedge HE-1 (induced by source paper "out of distribution detection with distance guarantee in deep generative models"):
  with members:
    - "conditional generative models are not robust"
    - "are generative deep models for novelty detection truly better"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "deep anomaly detection with outlier exposure"
    - "isolating sources of disentanglement in variational autoencoders"
    - "large scale gan training for high fidelity natural image synthesis"
    - "does your model know the digit 6 is not a cat a less biased evaluation of outlier detectors"
    - "variational inference of disentangled latent concepts from unlabeled observations"

Connected through hyperedge HE-2 (induced by source paper "unsupervised out of distribution detection with batch normalization"):
  with members:
    - "neural processes"
    - "are generative deep models for novelty detection truly better"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "deep anomaly detection with outlier exposure"
    - "anomaly detection with generative adversarial networks for multivariate time series"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "novelty detection with gan"

Connected through hyperedge HE-3 (induced by source paper "tapestry a blockchain based service for trusted interaction online"):
  with members:
    - "ethical dimensions of visualization research"
    - "composite behavioral modeling for identity theft detection in online social networks"
    - "a first look at identity management schemes on the blockchain"
    - "deep image retrieval learning global representations for image search"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "facenet a unified embedding for face recognition and clustering"
    - "a primer on neural network models for natural language processing"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "image anomaly detection with capsule networks and imbalanced datasets"):
  with members:
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "deep learning for classical japanese literature"
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "deep anomaly detection with outlier exposure"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "robust deep and inductive anomaly detection"
    - "dynamic routing between capsules"

Connected through hyperedge HE-5 (induced by source paper "castnet community attentive spatio temporal networks for opioid overdose forecasting"):
  with members:
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "deep forecast deep learning based spatio temporal forecasting"
    - "attention is all you need"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "automatic node selection for deep neural networks using group lasso regularization"

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)