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: "modular semantics and characteristics for bipolar weighted argumentation graphs"
  Abstract: This paper addresses the semantics of weighted argumentation graphs that are bipolar, i.e. contain both attacks and supports for arguments. It builds on previous work by Amgoud, Ben-Naim et. al. We study the various characteristics of acceptability semantics that have been introduced in these works, and introduce the notion of a modular acceptability semantics. A semantics is modular if it cleanly separates aggregation of attacking and supporting arguments (for a given argument $a$) from the computation of their influence on $a$'s initial weight. #R##N#We show that the various semantics for bipolar argumentation graphs from the literature may be analysed as a composition of an aggregation function with an influence function. Based on this modular framework, we prove general convergence and divergence theorems. We demonstrate that all well-behaved modular acceptability semantics converge for all acyclic graphs and that no sum-based semantics can converge for all graphs. In particular, we show divergence of Euler-based semantics (Amgoud et al.) for certain cyclic graphs. Further, we provide the first semantics for bipolar weighted graphs that converges for all graphs.

Connected through hyperedge HE-1 (induced by source paper "polynomial time updates of epistemic states in a fragment of probabilistic epistemic argumentation technical report"):
  with members:
    - "epistemic graphs for representing and reasoning with positive and negative influences of arguments"
    - "a polynomial time fragment of epistemic probabilistic argumentation technical report"
    - "a labelling framework for probabilistic argumentation"
    - "empirical evaluation of abstract argumentation supporting the need for bipolar and probabilistic approaches"

Connected through hyperedge HE-2 (induced by source paper "a tutorial for weighted bipolar argumentation with continuous dynamical systems and the java library attractor"):
  with members:
    - "afra argumentation framework with recursive attacks"
    - "graduality in argumentation"

Connected through hyperedge HE-3 (induced by source paper "a polynomial time fragment of epistemic probabilistic argumentation technical report"):
  with members:
    - "a labelling framework for probabilistic argumentation"

Connected through hyperedge HE-4 (induced by source paper "extending modular semantics for bipolar weighted argumentation technical report"):
  with members:
    - "graduality in argumentation"

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.AI(Artificial Intelligence)