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: "detection of anomalies in large scale accounting data using deep autoencoder networks"
  Abstract: Learning to detect fraud in large-scale accounting data is one of the long-standing challenges in financial statement audits or fraud investigations. Nowadays, the majority of applied techniques refer to handcrafted rules derived from known fraud scenarios. While fairly successful, these rules exhibit the drawback that they often fail to generalize beyond known fraud scenarios and fraudsters gradually find ways to circumvent them. To overcome this disadvantage and inspired by the recent success of deep learning, we propose the application of deep autoencoder neural networks to detect anomalous journal entries. We demonstrate that the trained network's reconstruction error obtainable for a journal entry and regularized by the entry's individual attribute probabilities can be interpreted as a highly adaptive anomaly assessment. Experiments on two real-world datasets of journal entries show the effectiveness of the approach resulting in high f1-scores of 32.93 (dataset A) and 16.95 (dataset B) and less false positive alerts compared to state of the art baseline methods. Initial feedback received by chartered accountants and fraud examiners underpinned the quality of the approach in capturing highly relevant accounting anomalies.

Connected through hyperedge HE-1 (induced by source paper "adversarial learning of deepfakes in accounting"):
  with members:
    - "detection of accounting anomalies in the latent space using adversarial autoencoder neural networks"
    - "text based editing of talking head video"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "voice conversion with conditional samplernn"
    - "adversarial text generation via feature mover s distance"
    - "adversarial attacks and defences a survey"
    - "rsgan face swapping and editing using face and hair representation in latent spaces"
    - "everybody dance now"

Connected through hyperedge HE-2 (induced by source paper "fraud detection in networks state of the art"):
  with members:
    - "unsupervised dictionary learning for anomaly detection"
    - "spotting collective behaviour of online frauds in customer reviews"
    - "deep learning for anomaly detection a survey"
    - "midas microcluster based detector of anomalies in edge streams"
    - "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data"
    - "no place to hide catching fraudulent entities in tensors"
    - "anomaly detection using one class neural networks"
    - "one class adversarial nets for fraud detection"

Connected through hyperedge HE-3 (induced by source paper "gan augmented text anomaly detection with sequences of deep statistics"):
  with members:
    - "on accurate and reliable anomaly detection for gas turbine combustors a deep learning approach"
    - "texygen a benchmarking platform for text generation models"
    - "anomaly detection via minimum likelihood generative adversarial networks"
    - "one class adversarial nets for fraud detection"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "good semi supervised learning that requires a bad gan"
    - "semi supervised learning with gans manifold invariance with improved inference"
    - "adversarial ranking for language generation"

Connected through hyperedge HE-4 (induced by source paper "autoencoders and generative adversarial networks for imbalanced sequence classification"):
  with members:
    - "doping generative data augmentation for unsupervised anomaly detection with gan"
    - "improved training of wasserstein gans"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-5 (induced by source paper "autoencoders and generative adversarial networks for anomaly detection for sequences"):
  with members:
    - "efficient gan based anomaly detection"
    - "improved training of wasserstein gans"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-6 (induced by source paper "detection of accounting anomalies in the latent space using adversarial autoencoder neural networks"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "one class adversarial nets for fraud detection"
    - "triple generative adversarial nets"
    - "solving the false positives problem in fraud prediction"
    - "empirical evaluation of rectified activations in convolutional network"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "aegr a simple approach to gradient reversal in autoencoders for network anomaly detection"):
  with members:
    - "a practical tutorial on autoencoders for nonlinear feature fusion taxonomy models software and guidelines"

Connected through hyperedge HE-8 (induced by source paper "anomaly detection via minimum likelihood generative adversarial networks"):
  with members:
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "robust deep and inductive anomaly detection"
    - "abnormal event detection in videos using generative adversarial nets"
    - "good semi supervised learning that requires a bad gan"
    - "likelihood estimation for generative adversarial networks"
    - "open category classification by adversarial sample generation"
    - "flow gan combining maximum likelihood and adversarial learning in generative models"
    - "improved techniques for training gans"

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.LG(Machine Learning)