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 "adversarial feature alignment avoid catastrophic forgetting in incremental task lifelong learning"):
  Abstract: Humans are able to master a variety of knowledge and skills with ongoing learning. By contrast, dramatic performance degradation is observed when new tasks are added to an existing neural network m...

Includes members:
  Member "continuous learning in single incremental task scenarios":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to continually learn")
  Member "deep adversarial attention alignment for unsupervised domain adaptation the benefit of target expectation maximization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "generative replay with feedback connections as a general strategy for continual learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a neural dirichlet process mixture model for task free continual learning")
  Member "memory aware synapses learning what not to forget":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "progressive neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "core50 a new dataset and benchmark for continuous object recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "incremental few shot object detection")
  Member "continual learning with deep generative replay":
    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 "incremental learning through deep adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")

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)