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 "patchfcn for intracranial hemorrhage detection"):
  Abstract: This paper studies the problem of detecting acute intracranial hemorrhage on head computed tomography (CT) scans. We formulate it as a pixel-wise labeling task of the frames that constitute a single head scan. The standard approach for this task is the fully convolutional network (FCN) which runs on the whole image at both training and test time. We propose a patch-based approach that controls the amount of context available to the FCN, based on the observation that when radiologists are interpreting CT scans, their judgment depends primarily on local cues and does not require the whole image context. To develop and validate the system, we collected a pixel-wise labeled dataset of 591 scans that covers a wide range of hemorrhage types and imaging conditions in the real world. We show that no pretraining from natural images is needed. By aggregating the pixel-wise labeling, our system is able to make region-level, frame-level, and stack-level decisions. Our final system approaches an expert radiologist performance with a high average precision (AP) of 96.5 +/- 1.3 % for hemorrhage classification on stack level while running at 23ms per frame

Includes members:
  Member "deep supervision with additional labels for retinal vessel segmentation task":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ophthalmic diagnosis using deep learning with fundus images a critical review")
  Member "autofocus layer for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi organ segmentation over partially labeled datasets with multi scale feature abstraction")
  Member "dilated residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "application of deep learning in neuroradiology automated detection of basal ganglia hemorrhage using 2d convolutional neural networks":
    also connected through other hyperedges:
      (none)
  Member "the cityscapes dataset for semantic urban scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "densely connected convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lossless compression of deep neural networks")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")

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)