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 "ternary feature masks continual learning without any forgetting"):
  Abstract: In this paper, we propose an approach without any forgetting to continual learning for the task-aware regime, where at inference the task-label is known. By using ternary masks we can upgrade a model to new tasks, reusing knowledge from previous tasks while not forgetting anything about them. Using masks prevents both catastrophic forgetting and backward transfer. We argue -- and show experimentally -- that avoiding the former largely compensates for the lack of the latter, which is rarely observed in practice. In contrast to earlier works, our masks are applied to the features (activations) of each layer instead of the weights. This considerably reduces the number of mask parameters to be added for each new task; with more than three orders of magnitude for most networks. The encoding of the ternary masks into two bits per feature creates very little overhead to the network, avoiding scalability issues. Our masks do not permit any changes to features which are used by previous tasks. As this may be too restrictive to allow learning of new tasks, we add task-specific feature normalization. This way, already learned features can adapt to the current task without changing the behavior of these features for previous tasks. Extensive experiments on several finegrained datasets and ImageNet show that our method outperforms current state-of-the-art while reducing memory overhead in comparison to weight-based approaches.

Includes members:
  Member "class incremental learning via deep model consolidation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an adaptive random path selection approach for incremental learning")
  Member "learn to grow a continual structure learning framework for overcoming catastrophic forgetting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a neural dirichlet process mixture model for task free continual learning")
  Member "continual learning a comparative study on how to defy forgetting in classification tasks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "rotate your networks better weight consolidation and less catastrophic forgetting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "continual universal object detection")
  Member "efficient lifelong learning with a gem":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "piggyback adapting a single network to multiple tasks by learning to mask weights":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "few shot few shot learning and the role of spatial attention")
  Member "learning to learn without forgetting by maximizing transfer and minimizing interference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "revisiting meta learning as supervised learning")
  Member "riemannian walk for incremental learning understanding forgetting and intransigence":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to 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.CV(Computer Vision and Pattern Recognition)