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 "carafe content aware reassembly of features"):
  Abstract: Feature upsampling is a key operation in a number of modern convolutional network architectures, e.g. feature pyramids. Its design is critical for dense prediction tasks such as object detection and semantic/instance segmentation. In this work, we propose Content-Aware ReAssembly of FEatures (CARAFE), a universal, lightweight and highly effective operator to fulfill this goal. CARAFE has several appealing properties: (1) Large field of view. Unlike previous works (e.g. bilinear interpolation) that only exploit sub-pixel neighborhood, CARAFE can aggregate contextual information within a large receptive field. (2) Content-aware handling. Instead of using a fixed kernel for all samples (e.g. deconvolution), CARAFE enables instance-specific content-aware handling, which generates adaptive kernels on-the-fly. (3) Lightweight and fast to compute. CARAFE introduces little computational overhead and can be readily integrated into modern network architectures. We conduct comprehensive evaluations on standard benchmarks in object detection, instance/semantic segmentation and inpainting. CARAFE shows consistent and substantial gains across all the tasks (1.2%, 1.3%, 1.8%, 1.1db respectively) with negligible computational overhead. It has great potential to serve as a strong building block for future research. It has great potential to serve as a strong building block for future research. Code and models are available at this https URL.

Includes members:
  Member "deep flow guided video inpainting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "background matting")
  Member "libra r cnn towards balanced learning for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "forward and backward information retention for accurate binary neural networks")
  Member "region proposal by guided anchoring":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "udd an underwater open sea farm object detection dataset for underwater robot picking")
  Member "meta sr a magnification arbitrary network for super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "metapruning meta learning for automatic neural network channel pruning")
  Member "mmdetection open mmlab detection toolbox and benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mixpath a unified approach for one shot neural architecture search")
  Member "foreground aware image inpainting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fine grained image to image transformation towards visual recognition")
  Member "mask scoring r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "proposal learning for semi supervised object detection")
  Member "guided upsampling network for real time semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "alignseg feature aligned segmentation networks")

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)