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 "multi scale dual branch fully convolutional network for hand parsing"):
  Abstract: Recently, fully convolutional neural networks (FCNs) have shown significant performance in image parsing, including scene parsing and object parsing. Different from generic object parsing tasks, hand parsing is more challenging due to small size, complex structure, heavy self-occlusion and ambiguous texture problems. In this paper, we propose a novel parsing framework, Multi-Scale Dual-Branch Fully Convolutional Network (MSDB-FCN), for hand parsing tasks. Our network employs a Dual-Branch architecture to extract features of hand area, paying attention on the hand itself. These features are used to generate multi-scale features with pyramid pooling strategy. In order to better encode multi-scale features, we design a Deconvolution and Bilinear Interpolation Block (DB-Block) for upsampling and merging the features of different scales. To address data imbalance, which is a common problem in many computer vision tasks as well as hand parsing tasks, we propose a generalization of Focal Loss, namely Multi-Class Balanced Focal Loss, to tackle data imbalance in multi-class classification. Extensive experiments on RHD-PARSING dataset demonstrate that our MSDB-FCN has achieved the state-of-the-art performance for hand parsing.

Includes members:
  Member "bag of tricks for image classification with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "progressive refinement a method of coarse to fine image parsing using stacked network":
    also connected through other hyperedges:
      (none)
  Member "multi scale spatially asymmetric recalibration for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rethinking normalization and elimination singularity in neural networks")
  Member "crossnet an end to end reference based super resolution network using cross scale warping":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "extended feature pyramid network for small object detection")
  Member "video object segmentation with joint re identification and attention aware mask propagation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "holistic instance level human parsing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hierarchical human parsing with typed part relation reasoning")
  Member "look into person self supervised structure sensitive learning and a new benchmark for human parsing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hierarchical human parsing with typed part relation reasoning")
  Member "learning to estimate 3d hand pose from single rgb images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rotation invariant mixed graphical model network for 2d hand pose estimation")

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)