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 "contcap a comprehensive framework for continual image captioning"):
  Abstract: While cutting-edge image captioning systems are increasingly describing an image coherently and exactly, recent progresses in continual learning allow deep learning systems to avoid catastrophic forgetting. However, the domain where image captioning working with continual learning is not exploited yet. We define the task in which we consolidate continual learning and image captioning as continual image captioning. In this work, we propose ContCap, a framework continually generating captions over a series of new tasks coming, seamlessly integrating continual learning into image captioning accompanied by tackling catastrophic forgetting. After proving catastrophic forgetting in image captioning, we employ freezing, knowledge distillation, and pseudo-labeling techniques to overcome the forgetting dilemma with the baseline is a simple fine-tuning scheme. We split MS-COCO 2014 dataset to perform experiments on incremental tasks without revisiting dataset of previously provided tasks. The experiments are designed to increase the degree of catastrophic forgetting and appraise the capacity of approaches. Experimental results show remarkable improvements in the performance on the old tasks, while the figure for the new task remains almost the same compared to fine-tuning. For example, pseudo-labeling increases CIDEr from 0.287 to 0.576 on the old task and 0.686 down to 0.657 BLEU1 on the new task.

Includes members:
  Member "fine grained continual learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "incremental learning techniques for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "modeling the background for incremental learning in semantic segmentation")
  Member "incremental classifier learning with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "brain inspired model for incremental learning using a few examples")
  Member "neural baby talk":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "incremental learning for semantic segmentation of large scale remote sensing data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "modeling the background for incremental learning in semantic segmentation")
  Member "continuous learning in single incremental task scenarios":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to continually learn")
  Member "show control and tell a framework for generating controllable and grounded captions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "say as you wish fine grained control of image caption generation with abstract scene graphs")
  Member "end to end incremental learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "incremental object detection via meta 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)