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: "attention based deep distance metric learning for aspect phrase grouping"
  Abstract: Aspect-phrase grouping is an important task for aspect finding in aspect-level sentiment analysis and it is a challenging problem due to polysemy and its context dependency. In this paper we propose an Attention-based Deep Distance Metric Learning (ADDML)method, which is more beneficial for clustering by considering aspect-phrase representation as well as context representation and their combination. Firstly, we feed word embeddings of aspect-phrases and its contexts into an attentionbased network to learn feature representation of contexts. Then, both of aspect-phrase embedding and context embedding as the input of a multilayer perceptron which is used to learn deep feature subspace, under which the distance of each intra-grouped pair is smaller and that of each intergrouped pair is bigger, respectively. After obtaining the learned representations, we use K-means to cluster them. Our extensive experimental study on four domain review datasets shows that the proposed method outperforms baseline 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.CL(Computation and Language)