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 object detection with attention rpn and multi relation detector"):
  Abstract: Conventional methods for object detection typically require a substantial amount of training data and preparing such high-quality training data is very labor-intensive. In this paper, we propose a novel few-shot object detection network that aims at detecting objects of unseen classes with only a few annotated examples. Central to our method are our Attention-RPN, Multi-Relation Detector and Contrastive Training strategy, which exploit the similarity between the few shot support set and query set to detect novel objects while suppressing false detection in the background. To train our network, we contribute a new dataset that contains 1000 categories of various objects with high-quality annotations. To the best of our knowledge, this is one of the first datasets specifically designed for few-shot object detection. Once our few-shot network is trained, it can detect objects of unseen classes without further training or fine-tuning. Our method is general and has a wide range of potential applications. We produce a new state-of-the-art performance on different datasets in the few-shot setting. The dataset link is this https URL.

Includes members:
  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 "revisiting local descriptor based image to class measure for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deepemd few shot image classification with differentiable earth mover s distance and structured classifiers")
  Member "few example object detection with model communication":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "edge labeling graph neural network for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deepemd few shot image classification with differentiable earth mover s distance and structured classifiers")
  Member "finding task relevant features for few shot learning by category traversal":
    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 "generating classification weights with gnn denoising autoencoders for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "incremental few shot object detection")
  Member "meta r cnn towards general solver for instance level few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "incremental object detection via meta learning")
  Member "one shot segmentation in clutter":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mechanical search multi step retrieval of a target object occluded by clutter")

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)