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: "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection"
  Abstract: Deep autoencoder has been extensively used for anomaly detection. Training on the normal data, the autoencoder is expected to produce higher reconstruction error for the abnormal inputs than the normal ones, which is adopted as a criterion for identifying anomalies. However, this assumption does not always hold in practice. It has been observed that sometimes the autoencoder "generalizes" so well that it can also reconstruct anomalies well, leading to the miss detection of anomalies. To mitigate this drawback for autoencoder based anomaly detector, we propose to augment the autoencoder with a memory module and develop an improved autoencoder called memory-augmented autoencoder, i.e. MemAE. Given an input, MemAE firstly obtains the encoding from the encoder and then uses it as a query to retrieve the most relevant memory items for reconstruction. At the training stage, the memory contents are updated and are encouraged to represent the prototypical elements of the normal data. At the test stage, the learned memory will be fixed, and the reconstruction is obtained from a few selected memory records of the normal data. The reconstruction will thus tend to be close to a normal sample. Thus the reconstructed errors on anomalies will be strengthened for anomaly detection. MemAE is free of assumptions on the data type and thus general to be applied to different tasks. Experiments on various datasets prove the excellent generalization and high effectiveness of the proposed MemAE.

Connected through hyperedge HE-1 (induced by source paper "memorizing comprehensively to learn adaptively unsupervised cross domain person re id with multi level memory"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "self training with progressive augmentation for unsupervised cross domain person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "multi level factorisation net for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"
    - "multi task mid level feature alignment network for unsupervised cross dataset person re identification"

Connected through hyperedge HE-2 (induced by source paper "learning and memorizing representative prototypes for 3d point cloud semantic and instance segmentation"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "3d bird s eye view instance segmentation"
    - "kpconv flexible and deformable convolution for point clouds"
    - "associatively segmenting instances and semantics in point clouds"
    - "large scale long tailed recognition in an open world"
    - "3d instance segmentation via multi task metric learning"
    - "deep hough voting for 3d object detection in point clouds"
    - "deepgcns can gcns go as deep as cnns"

Connected through hyperedge HE-3 (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"
    - "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"
    - "latent space autoregression for novelty detection"

Connected through hyperedge HE-4 (induced by source paper "self trained deep ordinal regression for end to end video anomaly detection"):
  with members:
    - "deep anomaly detection with deviation networks"
    - "anomaly detection in video sequence with appearance motion correspondence"
    - "real world anomaly detection in surveillance videos"
    - "object centric auto encoders and dummy anomalies for abnormal event detection in video"
    - "detecting abnormal events in video using narrowed normality clusters"
    - "adversarially learned one class classifier for novelty detection"
    - "latent space autoregression for novelty detection"
    - "unmasking the abnormal events in video"

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"
    - "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"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "enhancing the reliability of out of distribution image detection in neural networks"

Connected through hyperedge HE-6 (induced by source paper "history based anomaly detector an adversarial approach to anomaly detection"):
  with members:
    - "anomaly detection in video sequence with appearance motion correspondence"
    - "a survey on gans for anomaly detection"
    - "learning regularity in skeleton trajectories for anomaly detection in videos"
    - "deep structured cross modal anomaly detection"
    - "graph convolutional label noise cleaner train a plug and play action classifier for anomaly detection"
    - "input complexity and out of distribution detection with likelihood based generative models"
    - "deep learning for anomaly detection a survey"
    - "learning rich features for image manipulation detection"

Connected through hyperedge HE-7 (induced by source paper "inverse transform autoencoder for anomaly detection"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "skip ganomaly skip connected and adversarially trained encoder decoder anomaly detection"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "adversarially learned one class classifier for novelty detection"
    - "deep anomaly detection using geometric transformations"
    - "deep anomaly detection with outlier exposure"

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)