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 "unsupervised progressive learning and the stam architecture"):
  Abstract: We first pose the Unsupervised Progressive Learning (UPL) problem: learning salient representations from a non-stationary stream of unlabeled data in which the number of object classes increases with time. To solve the UPL problem we propose an architecture that involves a module called Self-Taught Associative Memory (STAM). Layered hierarchies of STAM modules learn based on a combination of online clustering, novelty detection, forgetting outliers, and storing only prototypical representations rather than specific examples. We evaluate STAM representations using clustering and classification tasks, relying on limited labeled data for the latter. Even though there are no prior approaches that are directly applicable to the UPL problem, we compare the STAM architecture to a couple of unsupervised and self-supervised deep learning approaches adapted in the UPL context.

Includes members:
  Member "lifelong machine learning with deep streaming linear discriminant analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "remind your neural network to prevent catastrophic forgetting")
  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 "continual learning with tiny episodic memories":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "continual learning in neural networks")
  Member "centroid networks for few shot clustering and unsupervised few shot classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a robot that learns connect four using game theory and demonstrations")
  Member "task free continual learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "a neural dirichlet process mixture model for task free 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)