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 "unified probabilistic deep continual learning through generative replay and open set recognition"):
  Abstract: We introduce a probabilistic approach to unify deep continual learning with open set recognition, based on variational Bayesian inference. Our single model combines a joint probabilistic encoder with a generative model and a linear classifier that get shared across sequentially arriving tasks. In order to successfully distinguish unseen unknown data from trained known tasks, we propose to bound the class specific approximate posterior by fitting regions of high density on the basis of correctly classified data points. These bounds are further used to significantly alleviate catastrophic forgetting by avoiding samples from low density areas in generative replay. Our approach requires no storing of old- or upfront knowledge of future data and is empirically validated on visual and audio tasks in class incremental, as well as cross-dataset scenarios across modalities.

Includes members:
  Member "continual lifelong learning with neural networks a review":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "deep learning for classical japanese literature":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improve sgd training via aligning min batches")
  Member "reducing network agnostophobia":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalized odin detecting out of distribution image without learning from out of distribution data")
  Member "life long disentangled representation learning with cross domain latent homologies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "are disentangled representations helpful for abstract visual reasoning")
  Member "interpreting and explaining deep neural networks for classification of audio signals":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sam the sensitivity of attribution methods to hyperparameters")
  Member "memory replay gans learning to generate images from new categories without forgetting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "continual universal object detection")
  Member "enhancing the reliability of out of distribution image detection in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "lifelong learning with dynamically expandable networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual 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)