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: "learning semantic specific graph representation for multi label image recognition"
  Abstract: Recognizing multiple labels of images is a practical and challenging task, and significant progress has been made by searching semantic-aware regions and modeling label dependency. However, current methods cannot locate the semantic regions accurately due to the lack of part-level supervision or semantic guidance. Moreover, they cannot fully explore the mutual interactions among the semantic regions and do not explicitly model the label co-occurrence. To address these issues, we propose a Semantic-Specific Graph Representation Learning (SSGRL) framework that consists of two crucial modules: 1) a semantic decoupling module that incorporates category semantics to guide learning semantic-specific representations and 2) a semantic interaction module that correlates these representations with a graph built on the statistical label co-occurrence and explores their interactions via a graph propagation mechanism. Extensive experiments on public benchmarks show that our SSGRL framework outperforms current state-of-the-art methods by a sizable margin, e.g. with an mAP improvement of 2.5%, 2.6%, 6.7%, and 3.1% on the PASCAL VOC 2007 & 2012, Microsoft-COCO and Visual Genome benchmarks, respectively. Our codes and models are available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "physical virtual collaboration graph network for station level metro ridership prediction"):
  with members:
    - "contextualized spatial temporal network for taxi origin destination demand prediction"
    - "graph convolutional label noise cleaner train a plug and play action classifier for anomaly detection"
    - "t gcn a temporal graph convolutional network for traffic prediction"
    - "graph convolutional neural networks for web scale recommender systems"
    - "graph to sequence learning using gated graph neural networks"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "hybrid knowledge routed modules for large scale object detection"
    - "describe and attend to track learning natural language guided structural representation and visual attention for object tracking"

Connected through hyperedge HE-2 (induced by source paper "knowledge graph transfer network for few shot recognition"):
  with members:
    - "knowledge embedded routing network for scene graph generation"
    - "variational prototyping encoder one shot learning with prototypical images"
    - "knowledge embedded representation learning for fine grained image recognition"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "hybrid knowledge routed modules for large scale object detection"
    - "visual semantic navigation using scene priors"
    - "low shot learning from imaginary data"

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)