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: "pixel level dense prediction without decoder"
  Abstract: Pixel-level dense prediction tasks such as keypoint estimation are dominated by encoder-decoder structures, where the decoder as a vital component is complex and computationally intensive. In contrast, we propose a fully decoding-free pixel-level dense prediction network called FlatteNet, in which the high dimensional tensor outputted by the backbone network is directly flattened to fit the desired output resolution. The proposed FlatteNet is end-to-end differentiable. By removing the decoder unit, FlatteNet requires much fewer parameters and lower computational complexity. We empirically demonstrate the effectiveness of the proposed network through competitive results in human pose estimation on MPII, semantic segmentation on PASCAL-Context, and object detection on PASCAL VOC. We hope that the proposed FlatteNet can serve as a simple and strong alternative of current mainstream decoder-based pixel-level dense prediction networks.

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)