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 "less forgetful learning for domain expansion in deep neural networks"):
  Abstract: Expanding the domain that deep neural network has already learned without accessing old domain data is a challenging task because deep neural networks forget previously learned information when learning new data from a new domain. In this paper, we propose a less-forgetful learning method for the domain expansion scenario. While existing domain adaptation techniques solely focused on adapting to new domains, the proposed technique focuses on working well with both old and new domains without needing to know whether the input is from the old or new domain. First, we present two naive approaches which will be problematic, then we provide a new method using two proposed properties for less-forgetful learning. Finally, we prove the effectiveness of our method through experiments on image classification tasks. All datasets used in the paper, will be released on our website for someone's follow-up study.

Includes members:
  Member "progressive neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "continual learning with deep generative replay":
    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 "overcoming catastrophic forgetting in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on contextual embeddings")
  Member "learning without forgetting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "joint face detection and alignment using multitask cascaded convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "learning transferable features with deep adaptation networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on contextual embeddings")
  Member "going deeper with convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)