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: "pooling hybrid representations for web structured data annotation"
  Abstract: Automatically identifying data types of web structured data is a key step in the process of web data integration. Web structured data is usually associated with entities or objects in a particular domain. In this paper, we aim to map attributes of an entity in a given domain to pre-specified classes of attributes in the same domain based on their values. To perform this task, we propose a hybrid deep learning network that relies on the format of the attributes' values. It does so without any pre-processing or using pre-defined hand-crafted features. The hybrid network combines sequence-based neural networks, namely convolutional neural networks (CNN) and recurrent neural networks (RNN), to learn the sequence structure of attributes' values. The CNN captures short-distance dependencies in these sequences through a sliding window approach, and the RNN captures long-distance dependencies by storing information of previous characters. These networks create different vector representations of the input sequence which are combined using a pooling layer. This layer applies a specific operation on these vectors in order to capture their most useful patterns for the task. Finally, on top of the pooling layer, a softmax function predicts the label of a given attribute value. We evaluate our strategy in four different web domains. The results show that the pooling network outperforms previous approaches, which use some kind of input pre-processing, in all domains.

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.DB(Databases)