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: "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.

Connected through hyperedge HE-1 (induced by source paper "3d aggregated faster r cnn for general lesion detection"):
  with members:
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "mini lesions detection on diabetic retinopathy images via large scale cnn features"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "applications of deep learning and reinforcement learning to biological data"
    - "focal loss for dense object detection"
    - "dssd deconvolutional single shot detector"
    - "deep lesion graphs in the wild relationship learning and organization of significant radiology image findings in a diverse large scale lesion database"
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "3d anchor free lesion detector on computed tomography scans"):
  with members:
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "objects as points"
    - "cornernet detecting objects as paired keypoints"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "focal loss for dense object detection"
    - "deep lesion graphs in the wild relationship learning and organization of significant radiology image findings in a diverse large scale lesion database"
    - "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"
    - "feature pyramid networks for object detection"

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)