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: "opinion dynamics and wisdom under out group discrimination"
  Abstract: We study a DeGroot-like opinion dynamics model in which agents may oppose other agents. As an underlying motivation, in our setup, agents want to adjust their opinions to match those of the agents of their ‘in-group’ and, in addition, they want to adjust their opinions to match the ‘inverse’ of those of the agents of their ‘out-group’. Our paradigm can account for persistent disagreement in connected societies as well as bi- and multi-polarization. Outcomes depend upon network structure and the choice of deviation function modeling the mode of opposition between agents. For a particular choice of deviation function, which we call soft opposition, we derive necessary and sufficient conditions for long-run polarization. We also consider social influence (who are the opinion leaders in the network?) as well as the question of wisdom in our naive learning paradigm, finding that wisdom is difficult to attain when there exist sufficiently strong negative relations between agents.11Earlier and more verbose working paper versions of this article can be found at http://arxiv.org/pdf/1306.3134 and the author’s personal website.

Connected through hyperedge HE-1 (induced by source paper "does my rebuttal matter insights from a major nlp conference"):
  with members:
    - "argument mining for understanding peer reviews"
    - "finding convincing arguments using scalable bayesian preference learning"
    - "a dataset of peer reviews peerread collection insights and nlp applications"
    - "a computational approach to politeness with application to social factors"

Connected through hyperedge HE-2 (induced by source paper "on the linearity of semantic change investigating meaning variation via dynamic graph models"):
  with members:
    - "freshman or fresher quantifying the geographic variation of internet language"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "statistically significant detection of linguistic change"
    - "that s sick dude automatic identification of word sense change across different timescales"
    - "distributed representations of words and phrases and their compositionality"
    - "exploiting similarities among languages for machine translation"

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.MA(Multiagent Systems)