human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "reinforced auto zoom net towards accurate and fast breast cancer segmentation in whole slide images"
  Abstract: Convolutional neural networks have led to significant breakthroughs in the domain of medical image analysis. However, the task of breast cancer segmentation in whole-slide images (WSIs) is still underexplored. WSIs are large histopathological images with extremely high resolution. Constrained by the hardware and field of view, using high-magnification patches can slow down the inference process and using low-magnification patches can cause the loss of information. In this paper, we aim to achieve two seemingly conflicting goals for breast cancer segmentation: accurate and fast prediction. We propose a simple yet efficient framework Reinforced Auto-Zoom Net (RAZN) to tackle this task. Motivated by the zoom-in operation of a pathologist using a digital microscope, RAZN learns a policy network to decide whether zooming is required in a given region of interest. Because the zoom-in action is selective, RAZN is robust to unbalanced and noisy ground truth labels and can efficiently reduce overfitting. We evaluate our method on a public breast cancer dataset. RAZN outperforms both single-scale and multi-scale baseline approaches, achieving better accuracy at low inference cost.

Connected through hyperedge HE-1 (induced by source paper "streaming convolutional neural networks for end to end learning with multi megapixel images"):
  with members:
    - "efficient memory management for gpu based deep learning systems"
    - "processing megapixel images with deep attention sampling models"
    - "predicting breast tumor proliferation from whole slide images the tupac16 challenge"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "neural image compression for gigapixel histopathology image analysis"
    - "attention based deep multiple instance learning"
    - "learning to zoom a saliency based sampling layer for neural networks"
    - "smoothgrad removing noise by adding noise"

Connected through hyperedge HE-2 (induced by source paper "da refinenet a dual input whole slide image segmentation algorithm based on attention"):
  with members:
    - "learning a discriminative feature network for semantic segmentation"
    - "multi resolution networks for semantic segmentation in whole slide images"
    - "assessment of breast cancer histology using densely connected convolutional networks"
    - "improving whole slide segmentation through visual context a systematic study"
    - "attention is all you need"
    - "deep learning applied to nlp"
    - "gp unet lesion detection from weak labels with a 3d regression network"
    - "squeeze and excitation networks"

Question: Please tell me which class the center node 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)