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 "towards single phase single stage detection of pulmonary nodules in chest ct imaging"):
  Abstract: Detection of pulmonary nodules in chest CT imaging plays a crucial role in early diagnosis of lung cancer. Manual examination is highly time-consuming and error prone, calling for computer-aided detection, both to improve efficiency and reduce misdiagnosis. Over the years, a range of systems have been proposed, mostly following a two-phase paradigm with: 1) candidate detection, 2) false positive reduction. Recently, deep learning has become a dominant force in algorithm development. As for candidate detection, prior art was mainly based on the two-stage Faster R-CNN framework, which starts with an initial sub-net to generate a set of class-agnostic region proposals, followed by a second sub-net to perform classification and bounding-box regression. In contrast, we abandon the conventional two-phase paradigm and two-stage framework altogether and propose to train a single network for end-to-end nodule detection instead, without transfer learning or further post-processing. Our feature learning model is a modification of the ResNet and feature pyramid network combined, powered by RReLU activation. The major challenge is the condition of extreme inter-class and intra-class sample imbalance, where the positives are overwhelmed by a large negative pool, which is mostly composed of easy and a handful of hard negatives. Direct training on all samples can seriously undermine training efficacy. We propose a patch-based sampling strategy over a set of regularly updating anchors, which narrows sampling scope to all positives and only hard negatives, effectively addressing this issue. As a result, our approach substantially outperforms prior art in terms of both accuracy and speed. Finally, the prevailing FROC evaluation over [1/8, 1/4, 1/2, 1, 2, 4, 8] false positives per scan, is far from ideal in real clinical environments. We suggest FROC over [1, 2, 4] false positives as a better metric.

Includes members:
  Member "soft nms improving object detection with one line of code":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "revisiting the sibling head in object detector")
  Member "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "no surprises training robust lung nodule detection for low dose ct scans by augmenting with adversarial attacks")
  Member "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multiresunet rethinking the u net architecture for multimodal biomedical image segmentation")
  Member "stylebank an explicit representation for neural image style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "collaborative distillation for ultra resolution universal style transfer")
  Member "training region based object detectors with online hard example mining":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "feature pyramid networks for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)