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: "a structured prediction approach for conditional meta learning"
  Abstract: Optimization-based meta-learning algorithms are a powerful class of methods for learning-to-learn applications such as few-shot learning. They tackle the limited availability of training data by leveraging the experience gained from previously observed tasks. However, when the complexity of the tasks distribution cannot be captured by a single set of shared meta-parameters, existing methods may fail to fully adapt to a target task. We address this issue with a novel perspective on conditional meta-learning based on structured prediction. We propose task-adaptive structured meta-learning (TASML), a principled estimator that weighs meta-training data conditioned on the target task to design tailored meta-learning objectives. In addition, we introduce algorithmic improvements to tackle key computational limitations of existing methods. Experimentally, we show that TASML outperforms state-of-the-art methods on benchmark datasets both in terms of accuracy and efficiency. An ablation study quantifies the individual contribution of model components and suggests useful practices for meta-learning.

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)