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: "pfa scannet pyramidal feature aggregation with synergistic learning for breast cancer metastasis analysis"
  Abstract: Automatic detection of cancer metastasis from whole slide images (WSIs) is a crucial step for following patient staging as well as prognosis. However, recent convolutional neural network (CNN) based approaches are struggling with the trade-off between accuracy and computation cost due to the difficulty in processing large-scale gigapixel images. To address this challenge, we propose a novel deep neural network, namely Pyramidal Feature Aggregation ScanNet (PFA-ScanNet) with pyramidal feature aggregation in both top-down and bottom-up paths. The discrimination capability of our detector is increased by leveraging the merit of contextual and spatial information from multi-scale features with larger receptive fields and less parameters. We also develop an extra decoder branch to synergistically learn the semantic information along with the detector, significantly improving the performance in recognizing the metastasis. Furthermore, a high-efficiency inference mechanism is designed with dense pooling layers, which allows dense and fast scanning for gigapixel WSI analysis. Our approach achieved the state-of-the-art FROC score of 89.1% on the Camelyon16 dataset, as well as competitive kappa score of 0.905 on the Camelyon17 leaderboard. In addition, our proposed method shows leading speed advantage over the state-of-the-art methods, which makes automatic analysis of breast cancer metastasis more applicable in the clinical usage.

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)