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 "cafenet class agnostic few shot edge detection network"):
  Abstract: We tackle a novel few-shot learning challenge, which we call few-shot semantic edge detection, aiming to localize crisp boundaries of novel categories using only a few labeled samples. We also present a Class-Agnostic Few-shot Edge detection Network (CAFENet) based on meta-learning strategy. CAFENet employs a semantic segmentation module in small-scale to compensate for lack of semantic information in edge labels. The predicted segmentation mask is used to generate an attention map to highlight the target object region, and make the decoder module concentrate on that region. We also propose a new regularization method based on multi-split matching. In meta-training, the metric-learning problem with high-dimensional vectors are divided into small subproblems with low-dimensional sub-vectors. Since there is no existing dataset for few-shot semantic edge detection, we construct two new datasets, FSE-1000 and SBD-$5^i$, and evaluate the performance of the proposed CAFENet on them. Extensive simulation results confirm the performance merits of the techniques adopted in CAFENet.

Includes members:
  Member "transductive few shot learning with meta learned confidence":
    also connected through other hyperedges:
      (none)
  Member "dense classification and implanting for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cross domain few shot classification via learned feature wise transformation")
  Member "bi directional cascade network for perceptual edge detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "concurrently extrapolating and interpolating networks for continuous model generation")
  Member "devil is in the edges learning semantic boundaries from noisy annotations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards bounding box free panoptic segmentation")
  Member "fss 1000 a 1000 class dataset for few shot segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the texture bias for few shot cnn segmentation")
  Member "tapnet neural network augmented with task adaptive projection for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meta learning across meta tasks for few shot learning")
  Member "dynamic feature fusion for semantic edge detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "panoptic edge detection")
  Member "panet few shot image semantic segmentation with prototype alignment":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the texture bias for few shot cnn segmentation")

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)