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: "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"
  Abstract: The recent turn towards quantitative text-as-data approaches in IR brought new ways to study the discursive landscape of world politics. Here seen as complementary to qualitative approaches, quantitative assessments have the advantage of being able to order and make comprehensible vast amounts of text. However, the validity of unsupervised methods applied to the types of text available in large quantities needs to be established before they can speak to other studies relying on text and discourse as data. In this paper, we introduce a new text corpus of United Nations Security Council (UNSC) speeches on Afghanistan between 2001 and 2017; we study this corpus through unsupervised topic modeling (LDA) with the central aim to validate the topic categories that the LDA identifies; and we discuss the added value, and complementarity, of quantitative text-as-data approaches. We set-up two tests using mixed- method approaches. Firstly, we evaluate the identified topics by assessing whether they conform with previous qualitative work on the development of the situation in Afghanistan. Secondly, we use network analysis to study the underlying social structures of what we will call 'speaker-topic relations' to see whether they correspondent to know divisions and coalitions in the UNSC. In both cases we find that the unsupervised LDA indeed provides valid and valuable outputs. In addition, the mixed-method approaches themselves reveal interesting patterns deserving future qualitative research. Amongst these are the coalition and dynamics around the 'women and human rights' topic as part of the UNSC debates on Afghanistan.

Connected through hyperedge HE-1 (induced by source paper "the un security council debates 1995 2017"):
  with members:
    - "understanding state preferences with text as data introducing the un general debate corpus"

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.SI(Social and Information Networks)