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 "prototype refinement network for few shot segmentation"):
  Abstract: Few-shot segmentation targets to segment new classes with few annotated images provided. It is more challenging than traditional semantic segmentation tasks that segment pre-defined classes with abundant annotated data. In this paper, we propose Prototype Refinement Network (PRNet) to attack the challenge of few-shot segmentation. PRNet learns to bidirectionally extract prototypes from both support and query images, which is different from existing methods. To extract representative prototypes of the new classes, we use adaptation and fusion for prototype refinement. The adaptation of PRNet is implemented by fine-tuning on the support set. Furthermore, prototype fusion is adopted to fuse support prototypes with query prototypes, incorporating the knowledge from both sides. Refined in this way, the prototypes become more discriminative in low-data regimes. Experiments on PASAL-$5^i$ and COCO-$20^i$ demonstrate the superiority of our method. Especially on COCO-$20^i$, PRNet significantly outperforms previous methods by a large margin of 13.1% in 1-shot setting and 17.4% in 5-shot setting respectively.

Includes members:
  Member "a baseline for few shot image classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimized generic feature learning for few shot classification across domains")
  Member "adaptive masked proxies for few shot segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the texture bias for few shot cnn segmentation")
  Member "panet few shot image semantic segmentation with prototype alignment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cafenet class agnostic few shot edge detection network")
  Member "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cafenet class agnostic few shot edge detection network")
  Member "reptile a scalable metalearning algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised domain adaptive object detection using forward backward cyclic adaptation")
  Member "encoder decoder with atrous separable convolution for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "sg one similarity guidance network for one shot semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cafenet class agnostic few shot edge detection network")
  Member "prototypical networks for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine 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)