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: "multi attribute proportional representation"
  Abstract: We consider the following problem in which a given number of items has to be chosen from a predefined set. Each item is described by a vector of attributes and for each attribute there is a desired distribution that the selected set should have. We look for a set that fits as much as possible the desired distributions on all attributes. Examples of applications include choosing members of a representative committee, where candidates are described by attributes such as sex, age and profession, and where we look for a committee that for each attribute offers a certain representation, i.e., a single committee that contains a certain number of young and old people, certain number of men and women, certain number of people with different professions, etc. With a single attribute the problem collapses to the apportionment problem for party-list proportional representation systems (in such case the value of the single attribute would be a political affiliation of a candidate). We study the properties of the associated subset selection rules, as well as their computation complexity.

Connected through hyperedge HE-1 (induced by source paper "electing a committee with dominance constraints"):
  with members:
    - "a rule for committee selection with soft diversity constraints"
    - "multiwinner elections with diversity constraints"
    - "multiwinner voting with fairness constraints"
    - "properties of multiwinner voting rules"
    - "finding a collective set of items from proportional multirepresentation to group recommendation"
    - "on the computation of fully proportional representation"

Connected through hyperedge HE-2 (induced by source paper "committee selection with attribute level preferences"):
  with members:
    - "proportional approval method using squared loads approval removal and coin flip approval transformation pamsac a new system of proportional representation using approval voting"
    - "campaign management under approval driven voting rules"
    - "justified representation in approval based committee voting"
    - "computational aspects of multi winner approval voting"

Connected through hyperedge HE-3 (induced by source paper "a rule for committee selection with soft diversity constraints"):
  with members:
    - "multiwinner elections with diversity constraints"
    - "group fairness in multiwinner voting"
    - "justified representation in approval based committee voting"

Connected through hyperedge HE-4 (induced by source paper "multiwinner elections with diversity constraints"):
  with members:
    - "mixed integer programming with convex concave constraints fixed parameter tractability and applications to multicovering and voting"
    - "group fairness in multiwinner voting"
    - "finding a collective set of items from proportional multirepresentation to group recommendation"
    - "on the computation of fully proportional representation"
    - "the complexity of fully proportional representation for single crossing electorates"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"

Connected through hyperedge HE-5 (induced by source paper "the price of diversity in assignment problems"):
  with members:
    - "diverse weighted bipartite b matching"
    - "multiwinner elections with diversity constraints"
    - "the conference paper assignment problem using order weighted averages to assign indivisible goods"
    - "finding fair and efficient allocations"
    - "social welfare in one sided matching markets without money"

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)