human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "fixing bias in reconstruction based anomaly detection with lipschitz discriminators"):
  Abstract: Anomaly detection is a problem of great interest in medicine, finance, and other fields where error and fraud need to be detected and corrected. Existing deep learning methods for anomaly detection almost universally rely on autoencoder reconstruction error. Here we show that this approach exhibits intrinsic biases, which can lead to undesirable results. We show that reconstruction-based methods are sensitive to outliers and are biased towards reconstructing any outliers in the training data, and outliers within the convex hull of the data. For these reasons, we introduce a new discriminator-based unsupervised Lipschitz anomaly discriminator (LAD), which does not suffer as much from these biases. We train a Wasserstein discriminator, similar to the ones used in GANs, to detect the difference between the training data and corruptions of the training data. We show that this procedure successfully detects unseen anomalies with guarantees on those that have a certain Wasserstein distance from the data or corrupted training set. These corrections allow us to surpass state of the art anomaly detection methods on MNIST and perform comparably on CIFAR10.

Includes members:
  Member "a survey on gans for anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "history based anomaly detector an adversarial approach to anomaly detection")
  Member "deep double descent where bigger models and more data hurt":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "memorization in overparameterized autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "balancedness and alignment are unlikely in linear neural networks")
  Member "deep learning for anomaly detection a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "ocgan one class novelty detection using gans with constrained latent representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compact surjective encoding autoencoder for unsupervised novelty detection")
  Member "efficient gan based anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "simple and effective prevention of mode collapse in deep one class classification")
  Member "ganomaly semi supervised anomaly detection via adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "arae adversarially robust training of autoencoders improves novelty detection")
  Member "adversarially learned one class classifier for novelty detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "compact surjective encoding autoencoder for unsupervised novelty detection")

Question: Please tell me which class the source paper 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)