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 "amad adversarial multiscale anomaly detection on high dimensional and time evolving categorical data"):
  Abstract: Anomaly detection is facing with emerging challenges in many important industry domains, such as cyber security and online recommendation and advertising. The recent trend in these areas calls for anomaly detection on time-evolving data with high-dimensional categorical features without labeled samples. Also, there is an increasing demand for identifying and monitoring irregular patterns at multiple resolutions. In this work, we propose a unified end-to-end approach to solve these challenges by combining the advantages of Adversarial Autoencoder and Recurrent Neural Network. The model learns data representations cross different scales with attention mechanisms, on which an enhanced two-resolution anomaly detector is developed for both instances and data blocks. Extensive experiments are performed over three types of datasets to demonstrate the efficacy of our method and its superiority over the state-of-art approaches.

Includes members:
  Member "neural related work summarization with a joint context driven attention mechanism":
    also connected through other hyperedges:
      (none)
  Member "ganomaly semi supervised anomaly detection via adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "compact surjective encoding autoencoder for unsupervised novelty detection")
  Member "adversarially learned anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "memory augmented generative adversarial networks for anomaly detection")
  Member "edge2vec representation learning using edge semantics for biomedical knowledge discovery":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "identifying illicit accounts in large scale e payment networks a graph representation learning approach")
  Member "generative adversarial networks an overview":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "novelty detection with gan":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "image based ood detector principles on graph based input data in human action recognition")
  Member "anomaly detection using one class neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sentinet detecting localized universal attacks against deep learning systems")

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)