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 "continual unsupervised representation learning"):
  Abstract: Continual learning aims to improve the ability of modern learning systems to deal with non-stationary distributions, typically by attempting to learn a series of tasks sequentially. Prior art in the field has largely considered supervised or reinforcement learning tasks, and often assumes full knowledge of task labels and boundaries. In this work, we propose an approach (CURL) to tackle a more general problem that we will refer to as unsupervised continual learning. The focus is on learning representations without any knowledge about task identity, and we explore scenarios when there are abrupt changes between tasks, smooth transitions from one task to another, or even when the data is shuffled. The proposed approach performs task inference directly within the model, is able to dynamically expand to capture new concepts over its lifetime, and incorporates additional rehearsal-based techniques to deal with catastrophic forgetting. We demonstrate the efficacy of CURL in an unsupervised learning setting with MNIST and Omniglot, where the lack of labels ensures no information is leaked about the task. Further, we demonstrate strong performance compared to prior art in an i.i.d setting, or when adapting the technique to supervised tasks such as incremental class learning.

Includes members:
  Member "unsupervised continual learning and self taught associative memory hierarchies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bayesian structure adaptation for continual learning")
  Member "continual lifelong learning with neural networks a review":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "continual learning via neural pruning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi scale weight sharing network for image recognition")
  Member "three scenarios for continual learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "triple memory networks a brain inspired method for continual learning")
  Member "task free continual learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on catastrophic interference in atari 2600 games")
  Member "re evaluating continual learning scenarios a categorization and case for strong baselines":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a neural dirichlet process mixture model for task free continual learning")
  Member "efficient lifelong learning with a gem":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "life long disentangled representation learning with cross domain latent homologies":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "are disentangled representations helpful for abstract visual reasoning")

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)