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 "the conference paper assignment problem using order weighted averages to assign indivisible goods"):
  Abstract: Motivated by the common academic problem of allocating papers to referees for conference reviewing we propose a novel mechanism for solving the assignment problem when we have a two sided matching problem with preferences from one side (the agents/reviewers) over the other side (the objects/papers) and both sides have capacity constraints. The assignment problem is a fundamental problem in both computer science and economics with application in many areas including task and resource allocation. We draw inspiration from multi-criteria decision making and voting and use order weighted averages (OWAs) to propose a novel and flexible class of algorithms for the assignment problem. We show an algorithm for finding a $\Sigma$-OWA assignment in polynomial time, in contrast to the NP-hardness of finding an egalitarian assignment. Inspired by this setting we observe an interesting connection between our model and the classic proportional multi-winner election problem in social choice.

Includes members:
  Member "properties of multiwinner voting rules":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "who is in your top three optimizing learning in elections with many candidates")
  Member "finding a collective set of items from proportional multirepresentation to group recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "evaluating approval based multiwinner voting in terms of robustness to noise")
  Member "computational aspects of multi winner approval voting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "evaluating approval based multiwinner voting in terms of robustness to noise")
  Member "a framework for optimizing paper matching":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a multi label classification method using a hierarchical and transparent representation for paper reviewer recommendation")
  Member "recommender systems for the conference paper assignment problem":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a multi label classification method using a hierarchical and transparent representation for paper reviewer recommendation")

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)