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 "continuous adaptation for interactive object segmentation by learning from corrections"):
  Abstract: In interactive object segmentation a user collaborates with a computer vision model to segment an object. Recent works rely on convolutional neural networks to predict the segmentation, taking the image and the corrections made by the user as input. By training on large datasets they offer strong performance, but they keep model parameters fixed at test time. Instead, we treat user corrections as training examples to update our model on-the-fly to the data at hand. This enables it to successfully adapt to the appearance of a particular test image, to distributions shifts in the whole test set, and even to large domain changes, where the imaging modality changes between training and testing. We extensively evaluate our method on 8 diverse datasets and improve over a fixed model on all of them. Our method shows the most dramatic improvements when training and testing domains differ, where it produces segmentation masks of the desired quality from 60-70% less user input. Furthermore we achieve state-of-the-art on four standard interactive segmentation datasets: PASCAL VOC12, GrabCut, DAVIS16 and Berkeley.

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 "large scale interactive object segmentation with human annotators":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "f brs rethinking backpropagating refinement for interactive segmentation")
  Member "efficient interactive annotation of segmentation datasets with polygon rnn":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")
  Member "task free continual learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on catastrophic interference in atari 2600 games")
  Member "interactive full image segmentation by considering all regions jointly":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")
  Member "a fully convolutional two stream fusion network for interactive image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "getting to 99 accuracy in interactive segmentation")
  Member "interactive video object segmentation in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "getting to 99 accuracy in interactive segmentation")
  Member "human centric indoor scene synthesis using stochastic grammar":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fast 3d indoor scene synthesis with discrete and exact layout pattern extraction")

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)