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 "scalable and interpretable one class svms with deep learning and random fourier features"):
  Abstract: One-class support vector machine (OC-SVM) for a long time has been one of the most effective anomaly detection methods and extensively adopted in both research as well as industrial applications. The biggest issue for OC-SVM is yet the capability to operate with large and high-dimensional datasets due to optimization complexity. Those problems might be mitigated via dimensionality reduction techniques such as manifold learning or autoencoder. However, previous work often treats representation learning and anomaly prediction separately. In this paper, we propose autoencoder based one-class support vector machine (AE-1SVM) that brings OC-SVM, with the aid of random Fourier features to approximate the radial basis kernel, into deep learning context by combining it with a representation learning architecture and jointly exploit stochastic gradient descent to obtain end-to-end training. Interestingly, this also opens up the possible use of gradient-based attribution methods to explain the decision making for anomaly detection, which has ever been challenging as a result of the implicit mappings between the input space and the kernel space. To the best of our knowledge, this is the first work to study the interpretability of deep learning in anomaly detection. We evaluate our method on a wide range of unsupervised anomaly detection tasks in which our end-to-end training architecture achieves a performance significantly better than the previous work using separate training.

Includes members:
  Member "efficient gan based anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "simple and effective prevention of mode collapse in deep one class classification")
  Member "unsupervised anomaly detection with generative adversarial networks to guide marker discovery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compact surjective encoding autoencoder for unsupervised novelty detection")
  Member "robust deep and inductive anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compact surjective encoding autoencoder for unsupervised novelty detection")
  Member "explaining nonlinear classification decisions with deep taylor decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "axiomatic attribution for deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial attacks and defenses on graphs a review and empirical study")
  Member "learning important features through propagating activation differences":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "importance driven deep learning system testing")
  Member "towards better understanding of gradient based attribution methods for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "explaining explanations axiomatic feature interactions for deep networks")
  Member "not just a black box learning important features through propagating activation differences":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning")

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)