human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "representing and reasoning with qualitative preferences for compositional systems"):
  Abstract: Many applications, e.g., Web service composition, complex system design, team formation, etc., rely on methods for identifying collections of objects or entities satisfying some functional requirement. Among the collections that satisfy the functional requirement, it is often necessary to identify one or more collections that are optimal with respect to user preferences over a set of attributes that describe the non-functional properties of the collection.#R##N##R##N#We develop a formalism that lets users express the relative importance among attributes and qualitative preferences over the valuations of each attribute. We define a dominance relation that allows us to compare collections of objects in terms of preferences over attributes of the objects that make up the collection. We establish some key properties of the dominance relation. In particular, we show that the dominance relation is a strict partial order when the intra-attribute preference relations are strict partial orders and the relative importance preference relation is an interval order.#R##N##R##N#We provide algorithms that use this dominance relation to identify the set of most preferred collections. We show that under certain conditions, the algorithms are guaranteed to return only (sound), all (complete), or at least one (weakly complete) of the most preferred collections. We present results of simulation experiments comparing the proposed algorithms with respect to (a) the quality of solutions (number of most preferred solutions) produced by the algorithms, and (b) their performance and efficiency. We also explore some interesting conjectures suggested by the results of our experiments that relate the properties of the user preferences, the dominance relation, and the algorithms.

Includes members:
  Member "graphical models for preference and utility":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "axiomatization of an importance index for k ary games")
  Member "ucp networks a directed graphical representation of conditional utilities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gradient based optimization for bayesian preference elicitation")
  Member "generic preferences over subsets of structured objects":
    also connected through other hyperedges:
      (none)
  Member "the computational complexity of dominance and consistency in cp nets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "complexity results for preference aggregation over m cp nets pareto and majority voting")
  Member "sorting and selection in posets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "decoy bandits dueling on a poset")
  Member "on graphical modeling of preference and importance":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "norm conflict resolution in stochastic domains")
  Member "cp nets a tool for representing and reasoning with conditional ceteris paribus preference statements":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on validating repairing and refining heuristic ml explanations")

Question: Please tell me which class the source paper 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)