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: "evaluating classifiers without expert labels"
  Abstract: Abstract This paper considers the challenge of evaluating a set of classi ers, asdone in shared task evaluations like the KDD Cup or NIST TREC, without ex-pert labels. While expert labels provide the traditional cornerstone for evaluatingstatistical learners, limited or expensive access to experts represents a practicalbottleneck. Instead, we seek methodology for estimating performance of the clas-si ers (relative and absolute) which is more scalable than expert labeling yet pre-serves high correlation with evaluation based on expert labels. We consider both:1) using only labels automatically generated by the classi ers themselves (blindevaluation); and 2) using labels obtained via crowdsourcing. While crowdsourcingmethods are lauded for scalability, using such data for evaluation raises seriousconcerns given the prevalence of label noise. In regard to blind evaluation, twobroad strategies are investigated: combine & score and score & combine. Combine& Score methods infer a single \pseudo-gold" label set by aggregating classi erlabels; classi ers are then evaluated based on this single pseudo-gold label set. Onthe other hand, score & combine methods: i) sample multiple label sets from clas-si er outputs, ii) evaluate classi ers on each label set, and iii) average classi erperformance across label sets. When additional crowd labels are also collected,we investigate two alternative avenues for exploiting them: 1) direct evaluation ofclassi ers; or 2) supervision of combine-and-score methods. To assess generality ofour techniques, classi er performance is measured using four common classi cationmetrics, with statistical signi cance tests establishing relative performance of theclassi ers for each metric. Finally, we measure both score and rank correlations be-tween estimated classi er performance vs. actual performance according to expertjudgments. Rigorous evaluation of classi ers from the TREC 2011 CrowdsourcingTrack shows reliable evaluation can be achieved without reliance on expert labels.

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)