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: "understanding meanings in multilingual customer feedback"
  Abstract: Understanding and being able to react to customer feedback is the most fundamental task in providing good customer service. However, there are two major obstacles for international companies to automatically detect the meaning of customer feedback in a global multilingual environment. Firstly, there is no widely acknowledged categorisation (classes) of meaning for customer feedback. Secondly, the applicability of one meaning categorisation, if it exists, to customer feedback in multiple languages is questionable. In this paper, we extracted representative real world samples of customer feedback from Microsoft Office customers in multiple languages, English, Spanish and Japanese,and concluded a five-class categorisation(comment, request, bug, complaint and meaningless) for meaning classification that could be used across languages in the realm of customer feedback analysis.

Connected through hyperedge HE-1 (induced by source paper "multimodal classification of urban micro events"):
  with members:
    - "xgboost a scalable tree boosting system"
    - "node2vec scalable feature learning for networks"
    - "civique using social media to detect urban emergencies"
    - "mining the minds of customers from online chat logs"
    - "deep residual learning for image recognition"
    - "deepwalk online learning of social representations"
    - "efficient estimation of word representations in vector space"

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)