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 "regularization shortcomings for continual learning"):
  Abstract: In classical machine learning, the data streamed to the algorithms is assumed to be independent and identically distributed. Otherwise, if the data distribution changes through time, the algorithm risks to remember only the data from the current state of the distribution and forget everything else. Continual learning is a sub-field of machine learning that aims to find automatic learning processes to solve non-iid problems. The main challenges of continual learning are two-fold. Firstly, to detect concept-drift in the distribution and secondly to remember what happened before a concept-drift. In this article, we study a specific case of continual learning approaches: \textit{the regularization method}. It consists of finding a smart regularization term that will protect important parameters from being modified to not forget. We show in this article, that in the context of multi-task learning for classification, this process does not learn to discriminate classes from different tasks. We propose theoretical reasoning to prove this shortcoming and illustrate it with examples and experiments with the "MNIST Fellowship" dataset.

Includes members:
  Member "online learned continual compression with stacked quantization modules":
    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 "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 "large scale incremental learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "modeling the background for incremental learning in semantic segmentation")
  Member "continual learning for robotics definition framework learning strategies opportunities and challenges":
    also connected through other hyperedges:
      (none)
  Member "online continual learning with maximally interfered retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "frosting weights for better continual training")
  Member "continual reinforcement learning deployed in real life using policy distillation and sim2real transfer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "from semantics to execution integrating action planning with reinforcement learning for robotic causal problem solving")
  Member "discorl continual reinforcement learning via policy distillation":
    also connected through other hyperedges:
      (none)
  Member "deep learning for classical japanese literature":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improve sgd training via aligning min batches")

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)