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: "arguing for decisions a qualitative model of decision making"
  Abstract: We develop a qualitative model of decision making with two aims: to describe how people make simple decisions and to enable computer programs to do the same. Current approaches based on Planning or Decisions Theory either ignore uncertainty and tradeoffs, or provide languages and algorithms that are too complex for this task. The proposed model provides a language based on rules, a semantics based on high probabilities and lexicographical preferences, and a transparent decision procedure where reasons for and against decisions interact. The model is no substitude for Decision Theory, yet for decisions that people find easy to explain it may provide an appealing alternative.

Connected through hyperedge HE-1 (induced by source paper "neural argument generation augmented with externally retrieved evidence"):
  with members:
    - "neural end to end learning for computational argumentation mining"
    - "winning on the merits the joint effects of content and style on debate outcomes"
    - "argument mining with structured svms and rnns"
    - "understanding and detecting supporting arguments of diverse types"
    - "sequence to sequence learning as beam search optimization"
    - "neural network based abstract generation for opinions and arguments"
    - "what to talk about and how selective generation using lstms with coarse to fine alignment"
    - "multi task sequence to sequence learning"

Connected through hyperedge HE-2 (induced by source paper "an argument based creative assistant for harmonic blending"):
  with members:
    - "modelling serendipity in a computational context"

Connected through hyperedge HE-3 (induced by source paper "a qualitative linear utility theory for spohn s theory of epistemic beliefs"):
  with members:
    - "an order of magnitude calculus"
    - "a general non probabilistic theory of inductive reasoning"
    - "from conditional oughts to qualitative decision theory"
    - "on the relation between kappa calculus and probabilistic reasoning"

Connected through hyperedge HE-4 (induced by source paper "using arguments for making decisions a possibilistic logic approach"):
  with members:
    - "automated reasoning using possibilistic logic semantics belief revision and variable certainty weights"
    - "nonmonotonic reasoning preferential models and cumulative logics"

Connected through hyperedge HE-5 (induced by source paper "qualitative decision making under possibilistic uncertainty toward more discriminating criteria"):
  with members:
    - "a possibilistic model for qualitative sequential decision problems under uncertainty in partially observable environments"
    - "a comparison of axiomatic approaches to qualitative decision making using possibility theory"

Connected through hyperedge HE-6 (induced by source paper "a unified setting for inference and decision an argumentation based approach"):
  with members:
    - "using arguments for making decisions a possibilistic logic approach"

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)