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: "shape aware feature extraction for instance segmentation"
  Abstract: Modern instance segmentation approaches mainly adopt a sequential paradigm - ``detect then segment'', as popularized by Mask R-CNN, which have achieved considerable progress. However, they usually struggle to segment huddled instances, i.e., instances which are crowded together. The essential reason is the detection step is only learned under box-level supervision. Without the guidance from the mask-level supervision, the features extracted from the regions containing huddled instances are noisy and ambiguous, which makes the detection problem ill-posed. To address this issue, we propose a new region-of-interest (RoI) feature extraction strategy, named Shape-aware RoIAlign, which focuses feature extraction within a region aligned well with the shape of the instance-of-interest rather than a rectangular RoI. We instantiate Shape-aware RoIAlign by introducing a novel refining module built upon Mask R-CNN, which takes the mask predicted by Mask R-CNN as the region to guide the computation of Shape-aware RoIAlign. Based on the RoI features re-computed by Shape-aware RoIAlign, the refining module updates the bounding box as well as the mask predicted by Mask R-CNN. Experimental results show that the refining module equipped with Shape-aware RoIAlign achieves consistent and remarkable improvements than Mask R-CNN models with different backbones, respectively, on the challenging COCO dataset. The code will be released.

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)