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 "dissecting catastrophic forgetting in continual learning by deep visualization"):
  Abstract: Interpreting the behaviors of Deep Neural Networks (usually considered as a black box) is critical especially when they are now being widely adopted over diverse aspects of human life. Taking the advancements from Explainable Artificial Intelligent, this paper proposes a novel technique called Auto DeepVis to dissect catastrophic forgetting in continual learning. A new method to deal with catastrophic forgetting named critical freezing is also introduced upon investigating the dilemma by Auto DeepVis. Experiments on a captioning model meticulously present how catastrophic forgetting happens, particularly showing which components are forgetting or changing. The effectiveness of our technique is then assessed; and more precisely, critical freezing claims the best performance on both previous and coming tasks over baselines, proving the capability of the investigation. Our techniques could not only be supplementary to existing solutions for completely eradicating catastrophic forgetting for life-long learning but also explainable.

Includes members:
  Member "incremental learning techniques for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "modeling the background for incremental learning in semantic segmentation")
  Member "contcap a comprehensive framework for continual image captioning":
    also connected through other hyperedges:
      (none)
  Member "a unified framework for lifelong learning in deep neural networks":
    also connected through other hyperedges:
      (none)
  Member "toward understanding catastrophic forgetting in continual learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "orthogonal gradient descent for continual learning")
  Member "lifelong gan continual learning for conditional image generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "focl feature oriented continual learning for generative models")
  Member "incremental learning for semantic segmentation of large scale remote sensing data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "modeling the background for incremental learning in semantic segmentation")
  Member "an empirical study of example forgetting during deep neural network learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning and memorizing representative prototypes for 3d point cloud semantic and instance segmentation")
  Member "progressive neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")

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)