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: "lpat learning to predict adaptive threshold for weakly supervised temporal action localization"
  Abstract: Recently, Weakly-supervised Temporal Action Localization (WTAL) has been densely studied because it can free us from costly annotating temporal boundaries of actions. One prevalent strategy is obtaining action score sequences over time and then truncating segments of scores higher than a fixed threshold at every kept snippet. However, the threshold is not modeled in the training process and manually setting the threshold introduces expert knowledge, which damages the coherence of systems and makes it unfair for comparisons. In this paper, we propose to adaptively set the threshold at each snippet to be its background score, which can be learned to predict (LPAT). In both training and testing time, the predicted threshold is leveraged to localize action segments and the scores of these segments are allocated for video classification. We also identify an important constraint to improve the confidence of generated proposals, and model it as a novel loss term, which facilitates the video classification loss to improve models' localization ability. As such, our LPAT model is able to generate accurate action proposals with only video-level supervision. Extensive experiments on two standard yet challenging datasets, i.e., THUMOS'14 and ActivityNet1.2, show significant improvement over state-of-the-art methods.

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)