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: "active learning with siamese twins for sequence tagging"
  Abstract: Deep learning, in general, and natural language processing methods, in particular, rely heavily on annotated samples to achieve good performance. However, manually annotating data is expensive and time consuming. Active Learning (AL) strategies reduce the need for huge volumes of labelled data by iteratively selecting a small number of examples for manual annotation based on their estimated utility in training the given model. In this paper, we argue that since AL strategies choose examples independently, they may potentially select similar examples, all of which do not aid in the learning process. We propose a method, referred to as Active$\mathbf{^2}$ Learning (A$\mathbf{^2}$L), that actively adapts to the sequence tagging model being trained, to further eliminate such redundant examples chosen by an AL strategy. We empirically demonstrate that A$\mathbf{^2}$L improves the performance of state-of-the-art AL strategies on different sequence tagging tasks. Furthermore, we show that A$\mathbf{^2}$L is widely applicable by using it in conjunction with different AL strategies and sequence tagging models. We demonstrate that the proposed A$\mathbf{^2}$L able to reach full data F-score with $\approx\mathbf{2-16 \%}$ less data compared to state-of-art AL strategies on different sequence tagging datasets.

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)