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: "computational international relations what can programming coding and internet research do for the discipline"
  Abstract: Computational Social Science emerged as a highly technical and popular discipline in the last few years, owing to the substantial advances in communication technology and daily production of vast quantities of personal data. As per capita data production significantly increased in the last decade, both in terms of its size, bytes, as well as its detail, heart rate monitors, Internet connected appliances, smartphones, social scientists ability to extract meaningful social, political and demographic information from digital data also increased. A vast methodological gap exists in computational international relations, or ComInt, which refers to the use of one or a combination of tools such as data mining, natural language processing, automated text analysis, web scraping, geospatial analysis and machine learning to provide larger and better organized data to test more advanced theories of IR. After providing an overview of the potentials of computational IR and how an IR scholar can establish technical proficiency in computer science, such as starting with Python, R, QGis, ArcGIS or Github, this paper will focus on some of the author's works in providing an idea for IR students on how to think about computational IR. The paper argues that computational methods transcend the methodological schism between qualitative and quantitative approaches and form a solid foundation for building truly multi method research design.

Connected through hyperedge HE-1 (induced by source paper "discursive landscapes and unsupervised topic modeling in ir a validation of text as data approaches through a new corpus of un security council speeches on afghanistan"):
  with members:
    - "disunited nations a multiplex network approach to detecting preference affinity blocs using texts and votes"
    - "detecting policy preferences and dynamics in the un general debate with neural word embeddings"
    - "topology analysis of international networks based on debates in the united nations"

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.CY(Computers and Society)