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: "et usb transformer based sequential behavior modeling for inbound customer service"
  Abstract: Deep-Learning based models with attention mechanism has achieved exceptional results for many tasks, including language tasks and recommender system. In this study, we focus on inbound calls prediction for customer service, whereas previous works put emphasis on the phone agents allocation. Furthermore, a common method to deal with user's history behaviors is to extract all kinds of aggregated features over time. Nevertheless, the method may fail to detect users' sequential pattern. Therefore, we incorporate users' sequential features along with non-sequential features and apply the encoder of Transformer, one such powerful self-attention network model, to capture the information underlying those behavior sequence. We propose our approach: ET-USB, which is helpful for different business scenarios in Cathay Financial Holdings. In practice, we test and prove that the proposed network structure for processing different dimension of behavior data could demonstrate the superiority comparing with other deep-learning based models.

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)