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: "the diverse cohort selection problem"
  Abstract: How should a firm allocate its limited interviewing resources to select the optimal cohort of new employees from a large set of job applicants? How should that firm allocate cheap but noisy resume screenings and expensive but in-depth in-person interviews? We view this problem through the lens of combinatorial pure exploration (CPE) in the multi-armed bandit setting, where a central learning agent performs costly exploration of a set of arms before selecting a final subset with some combinatorial structure. We generalize a recent CPE algorithm to the setting where arm pulls can have different costs and return different levels of information. We then prove theoretical upper bounds for a general class of arm-pulling strategies in this new setting. We apply our general algorithm to a real-world problem with combinatorial structure: incorporating diversity into university admissions. We take real data from admissions at one of the largest US-based computer science graduate programs and show that a simulation of our algorithm produces a cohort with hiring overall utility while spending comparable budget to the current admissions process at that university.

Connected through hyperedge HE-1 (induced by source paper "an algorithm for multi attribute diverse matching"):
  with members:
    - "balancing relevance and diversity in online bipartite matching via submodularity"
    - "the conference paper assignment problem using order weighted averages to assign indivisible goods"
    - "learning mixtures of submodular shells with application to document summarization"

Connected through hyperedge HE-2 (induced by source paper "group fairness in bandit arm selection"):
  with members:
    - "achieving fairness in the stochastic multi armed bandit problem"
    - "reinforcement learning with fairness constraints for resource distribution in human robot teams"
    - "making the cut a bandit based approach to tiered interviewing"
    - "multi stakeholder recommendation and its connection to multi sided fairness"
    - "incorporating behavioral constraints in online ai systems"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "the frontiers of fairness in machine learning"
    - "fairness of exposure in rankings"

Connected through hyperedge HE-3 (induced by source paper "making the cut a bandit based approach to tiered interviewing"):
  with members:
    - "a voting based system for ethical decision making"
    - "learning to hire teams"
    - "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization"
    - "multiple identifications in multi armed bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-4 (induced by source paper "algorithms for optimal diverse matching"):
  with members:
    - "migration as submodular optimization"
    - "diverse weighted bipartite b matching"
    - "learning mixtures of submodular shells with application to document summarization"

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.LG(Machine Learning)