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: "group fairness in multiwinner voting"
  Abstract: We study multiwinner voting problems when there is an additional requirement that the selected committee should be fair with respect to attributes such as gender, ethnicity, or political parties. Every setting of an attribute gives rise to a group, and the goal is to ensure that each group is neither over nor under represented in the selected committee. Prior work has largely focused on designing specialized score functions that lead to a precise level of representation with respect to disjoint attributes (e.g., only political affiliation). Here we propose a general algorithmic framework that allows the use of any score function and can guarantee flexible notions of fairness with respect to multiple, non-disjoint attributes (e.g., political affiliation and gender). Technically, we study the complexity of this constrained multiwinner voting problem subject to group-fairness constraints for monotone submodular score functions. We present approximation algorithms and hardness of approximation results for various attribute set structures and score functions.

Connected through hyperedge HE-1 (induced by source paper "fair correlation clustering"):
  with members:
    - "coresets for clustering with fairness constraints"
    - "summarizing user generated textual content motivation and methods for fairness in algorithmic summaries"
    - "improved algorithms for correlation clustering with local objectives"
    - "near neighbor who is the fairest of them all"
    - "correlation clustering with adaptive similarity queries"
    - "fair algorithms for clustering"
    - "scalable fair clustering"
    - "achieving fairness in determining medicaid eligibility through fairgroup construction"

Connected through hyperedge HE-2 (induced by source paper "committee scoring rules axiomatic characterization and hierarchy"):
  with members:
    - "what do multiwinner voting rules do an experiment over the two dimensional euclidean domain"
    - "the condorcet principle for multiwinner elections from shortlisting to proportionality"
    - "monotonicity axioms in approval based multi winner voting rules"
    - "multiwinner elections with diversity constraints"
    - "consistent approval based multi winner rules"
    - "multiwinner voting with fairness constraints"
    - "robustness among multiwinner voting rules"
    - "proportional approval voting harmonic k median and negative association"

Connected through hyperedge HE-3 (induced by source paper "removing algorithmic discrimination with minimal individual error"):
  with members:
    - "progressive growing of gans for improved quality stability and variation"
    - "ranking with fairness constraints"
    - "learning non discriminatory predictors"
    - "fairness in reinforcement learning"
    - "equality of opportunity in supervised learning"
    - "how to be fair and diverse"
    - "efficient estimation of word representations in vector space"
    - "fairness through awareness"

Connected through hyperedge HE-4 (induced by source paper "a rule for committee selection with soft diversity constraints"):
  with members:
    - "multiwinner elections with diversity constraints"
    - "multi attribute proportional representation"
    - "justified representation in approval based committee voting"

Connected through hyperedge HE-5 (induced by source paper "modeling epistemological principles for bias mitigation in ai systems an illustration in hiring decisions"):
  with members:
    - "ranking with fairness constraints"

Connected through hyperedge HE-6 (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"
    - "multi attribute proportional representation"
    - "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"

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.CY(Computers and Society)