human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "dissecting catastrophic forgetting in continual learning by deep visualization"):
  with members:
    - "incremental learning techniques for semantic segmentation"
    - "a unified framework for lifelong learning in deep neural networks"
    - "toward understanding catastrophic forgetting in continual learning"
    - "lifelong gan continual learning for conditional image generation"
    - "incremental learning for semantic segmentation of large scale remote sensing data"
    - "an empirical study of example forgetting during deep neural network learning"
    - "progressive neural networks"
    - "continual learning through synaptic intelligence"

Question: Please tell me which class the center node 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)