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 "few shot learning with contextual cueing for object recognition in complex scenes"):
  Abstract: Few-shot Learning aims to recognize new concepts from a small number of training examples. Recent work mainly tackle this problem by improving visual features, feature transfer and meta-training algorithms. In this work, we propose to explore a complementary direction by using scene context semantics to learn and recognize new concepts more easily. Whereas a few visual examples cannot cover all intra-class variations, contextual cueing offers a complementary signal to classify instances with unseen features or ambiguous objects. More specifically, we propose a Class-conditioned Context Attention Module (CCAM) that learns to weight the most important context elements while learning a particular concept. We additionally propose a flexible gating mechanism to ground visual class representations in context semantics. #R##N#We conduct extensive experiments on Visual Genome dataset, and we show that compared to a visual-only baseline, our model improves top-1 accuracy by 20.47% and 9.13% in 5-way 1-shot and 5-way 5-shot, respectively; and by 20.42% and 12.45% in 20-way 1-shot and 20-way 5-shot, respectively.

Includes members:
  Member "baby steps towards few shot learning with multiple semantics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tafssl task adaptive feature sub space learning for few shot classification")
  Member "dense classification and implanting for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cross domain few shot classification via learned feature wise transformation")
  Member "context aware zero shot learning for object recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "few shot learning via saliency guided hallucination of samples":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "incremental few shot object detection")
  Member "few shot learning with localization in realistic settings":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deepemd few shot image classification with differentiable earth mover s distance and structured classifiers")
  Member "revisiting local descriptor based image to class measure for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deepemd few shot image classification with differentiable earth mover s distance and structured classifiers")
  Member "tafe net task aware feature embeddings for low shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "automated relational meta learning")
  Member "image deformation meta networks for one shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "incremental few shot object detection")

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)