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: "a maximum likelihood approach for selecting sets of alternatives"
  Abstract: We consider the problem of selecting a subset of alternatives given noisy evaluations of the relative strength of different alternatives. We wish to select a k-subset (for a given k) that provides a maximum likelihood estimate for one of several objectives, e.g., containing the strongest alternative. Although this problem is NP-hard, we show that when the noise level is sufficiently high, intuitive methods provide the optimal solution. We thus generalize classical results about singling out one alternative and identifying the hidden ranking of alternatives by strength. Extensive experiments show that our methods perform well in practical settings.

Connected through hyperedge HE-1 (induced by source paper "evaluating approval based multiwinner voting in terms of robustness to noise"):
  with members:
    - "a quantitative analysis of multi winner rules"
    - "consistent approval based multi winner rules"
    - "multiwinner approval rules as apportionment methods"
    - "finding a collective set of items from proportional multirepresentation to group recommendation"
    - "justified representation in approval based committee voting"
    - "computational aspects of multi winner approval voting"
    - "common voting rules as maximum likelihood estimators"

Connected through hyperedge HE-2 (induced by source paper "a quantitative analysis of multi winner rules"):
  with members:
    - "what do multiwinner voting rules do an experiment over the two dimensional euclidean domain"
    - "proportionality and the limits of welfarism"
    - "group fairness in committee selection"
    - "fair allocation of indivisible public goods"
    - "the condorcet principle for multiwinner elections from shortlisting to proportionality"
    - "consistent approval based multi winner rules"
    - "robustness among multiwinner voting rules"
    - "what do we elect committees for a voting committee model for multi winner rules"

Connected through hyperedge HE-3 (induced by source paper "truthful mechanisms for ownership transfer with expert advice"):
  with members:
    - "better strategyproof mechanisms without payments or prior an analytic approach"
    - "finding a collective set of items from proportional multirepresentation to group recommendation"
    - "social welfare in one sided matchings random priority and beyond"
    - "truthful approximations to range voting"

Connected through hyperedge HE-4 (induced by source paper "a mathematical model for optimal decisions in a representative democracy"):
  with members:
    - "proxy voting for better outcomes"
    - "what do we elect committees for a voting committee model for multi winner rules"
    - "common voting rules as maximum likelihood estimators"

Connected through hyperedge HE-5 (induced by source paper "a statistical decision theoretic framework for social choice"):
  with members:
    - "random utility theory for social choice"
    - "common voting rules as maximum likelihood estimators"

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)