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: "linear context transform block"
  Abstract: Squeeze-and-Excitation (SE) block presents a channel attention mechanism for modeling the global context via explicitly capturing dependencies between channels. However, we still poorly understand for SE block. In this work, we first revisit the SE block and present a detailed empirical study of the relationship between global context and attention distribution, based on which we further propose a simple yet effective module. We call this module Linear Context Transform (LCT) block, which implicitly captures dependencies between channels and linearly transforms the global context of each channel. LCT block is extremely lightweight with negligible parameters and computations. Extensive experiments show that LCT block outperforms SE block in image classification on ImageNet and object detection/segmentation on COCO across many models. Moreover, we also demonstrate that LCT block can yield consistent performance gains for existing state-of-the-art detection architectures. For examples, LCT block brings 1.5$\sim$1.7% AP$^{bbox}$ and 1.0%$\sim$1.2% AP$^{mask}$ gains independently of the detector strength on COCO benchmark. We hope our work will provide a new insight into the channel attention

Connected through hyperedge HE-1 (induced by source paper "highly efficient salient object detection with 100k parameters"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "searching for mobilenetv3"
    - "a simple pooling based design for real time salient object detection"
    - "dabnet depth wise asymmetric bottleneck for real time semantic segmentation"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "deeply supervised salient object detection with short connections"
    - "egnet edge guidance network for salient 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.LG(Machine Learning)