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: "multiwinner elections with diversity constraints"
  Abstract: We develop a model of multiwinner elections that combines performance-based measures of the quality of the committee (such as, e.g., Borda scores of the committee members) with diversity constraints. Specifically, we assume that the candidates have certain attributes (such as being a male or a female, being junior or senior, etc.) and the goal is to elect a committee that, on the one hand, has as high a score regarding a given performance measure, but that, on the other hand, meets certain requirements (e.g., of the form "at least $30\%$ of the committee members are junior candidates and at least $40\%$ are females"). We analyze the computational complexity of computing winning committees in this model, obtaining polynomial-time algorithms (exact and approximate) and NP-hardness results. We focus on several natural classes of voting rules and diversity constraints.

Connected through hyperedge HE-1 (induced by source paper "algorithmic fairness"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "fairface face attribute dataset for balanced race gender and age"
    - "fair regression quantitative definitions and reduction based algorithms"
    - "putting fairness principles into practice challenges metrics and improvements"
    - "generative adversarial networks for mitigating biases in machine learning systems"
    - "improved adversarial learning for fair classification"
    - "compositional fairness constraints for graph embeddings"
    - "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"

Connected through hyperedge HE-2 (induced by source paper "on the fairness of time critical influence maximization in social networks"):
  with members:
    - "group fairness in influence maximization"
    - "learning optimal and fair decision trees for non discriminative decision making"
    - "gaps in information access in social networks"
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "equality of voice towards fair representation in crowdsourced top k recommendations"
    - "fair allocation of indivisible public goods"
    - "approximate maximin shares for groups of agents"
    - "democratic fair allocation of indivisible goods"

Connected through hyperedge HE-3 (induced by source paper "group fairness in influence maximization"):
  with members:
    - "fair allocation of indivisible public goods"
    - "groupwise maximin fair allocation of indivisible goods"
    - "multi objective maximization of monotone submodular functions with cardinality constraint"
    - "diverse weighted bipartite b matching"
    - "approximate maximin shares for groups of agents"
    - "stochastic submodular maximization the case of coverage functions"
    - "gradient methods for submodular maximization"
    - "democratic fair allocation of indivisible goods"

Connected through hyperedge HE-4 (induced by source paper "electing a committee with dominance constraints"):
  with members:
    - "a rule for committee selection with soft diversity constraints"
    - "multiwinner voting with fairness constraints"
    - "multi attribute proportional representation"
    - "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-5 (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"
    - "consistent approval based multi winner rules"
    - "multiwinner voting with fairness constraints"
    - "group fairness in multiwinner voting"
    - "robustness among multiwinner voting rules"
    - "proportional approval voting harmonic k median and negative association"

Connected through hyperedge HE-6 (induced by source paper "a rule for committee selection with soft diversity constraints"):
  with members:
    - "group fairness in multiwinner voting"
    - "multi attribute proportional representation"
    - "justified representation in approval based committee voting"

Connected through hyperedge HE-7 (induced by source paper "the diverse cohort selection problem"):
  with members:
    - "diverse weighted bipartite b matching"
    - "calibrated fairness in bandits"
    - "the conference paper assignment problem using order weighted averages to assign indivisible goods"
    - "fairness in learning classic and contextual bandits"
    - "learning to hire teams"
    - "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-8 (induced by source paper "the price of diversity in assignment problems"):
  with members:
    - "diverse weighted bipartite b matching"
    - "the conference paper assignment problem using order weighted averages to assign indivisible goods"
    - "finding fair and efficient allocations"
    - "multi attribute proportional representation"
    - "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.GT(Computer Science and Game Theory)