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: "diverse weighted bipartite b matching"
  Abstract: Bipartite matching, where agents on one side of a market are matched to agents or items on the other, is a classical problem in computer science and economics, with widespread application in healthcare, education, advertising, and general resource allocation. A practitioner's goal is typically to maximize a matching market's economic efficiency, possibly subject to some fairness requirements that promote equal access to resources. A natural balancing act exists between fairness and efficiency in matching markets, and has been the subject of much research. #R##N#In this paper, we study a complementary goal---balancing diversity and efficiency---in a generalization of bipartite matching where agents on one side of the market can be matched to sets of agents on the other. Adapting a classical definition of the diversity of a set, we propose a quadratic programming-based approach to solving a supermodular minimization problem that balances diversity and total weight of the solution. We also provide a scalable greedy algorithm with theoretical performance bounds. We then define the price of diversity, a measure of the efficiency loss due to enforcing diversity, and give a worst-case theoretical bound. Finally, we demonstrate the efficacy of our methods on three real-world datasets, and show that the price of diversity is not bad in practice.

Connected through hyperedge HE-1 (induced by source paper "forming diverse teams from sequentially arriving people"):
  with members:
    - "an algorithm for multi attribute diverse matching"
    - "algorithms for optimal diverse matching"
    - "streaming non monotone submodular maximization personalized video summarization on the fly"
    - "ranking ideas for diversity and quality"
    - "fast algorithms for online stochastic convex programming"
    - "learning mixtures of submodular shells with application to document summarization"

Connected through hyperedge HE-2 (induced by source paper "finding fair and efficient allocations when valuations don t add up"):
  with members:
    - "equitable allocations of indivisible chores"
    - "fair division of indivisible goods among strategic agents"
    - "almost envy freeness with general valuations"
    - "how to be fair and diverse"

Connected through hyperedge HE-3 (induced by source paper "stable matchings with diversity constraints affirmative action is beyond np"):
  with members:
    - "from matching with diversity constraints to matching with regional quotas"
    - "parameterized complexity of stable roommates with ties and incomplete lists through the lens of graph parameters"
    - "on treewidth and stable marriage"

Connected through hyperedge HE-4 (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"
    - "approximate maximin shares for groups of agents"
    - "multiwinner elections with diversity constraints"
    - "stochastic submodular maximization the case of coverage functions"
    - "gradient methods for submodular maximization"
    - "democratic fair allocation of indivisible goods"

Connected through hyperedge HE-5 (induced by source paper "algorithms for optimal diverse matching"):
  with members:
    - "migration as submodular optimization"
    - "the diverse cohort selection problem"
    - "learning mixtures of submodular shells with application to document summarization"

Connected through hyperedge HE-6 (induced by source paper "the diverse cohort selection problem"):
  with members:
    - "multiwinner elections with diversity constraints"
    - "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-7 (induced by source paper "the price of diversity in assignment problems"):
  with members:
    - "multiwinner elections with diversity constraints"
    - "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.DS(Data Structures and Algorithms)