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: "how effective can simple ordinal peer grading be"
  Abstract: Ordinal peer grading has been proposed as a simple and scalable solution for computing reliable information about student performance in massive open online courses. The idea is to outsource the grading task to the students themselves as follows. After the end of an exam, each student is asked to rank ---in terms of quality--- a bundle of exam papers by fellow students. An aggregation rule will then combine the individual rankings into a global one that contains all students. We define a broad class of simple aggregation rules and present a theoretical framework for assessing their effectiveness. When statistical information about the grading behaviour of students is available, the framework can be used to compute the optimal rule from this class with respect to a series of performance objectives. For example, a natural rule known as Borda is proved to be optimal when students grade correctly. In addition, we present extensive simulations and a field experiment that validate our theory and prove it to be extremely accurate in predicting the performance of aggregation rules even when only rough information about grading behaviour is available.

Connected through hyperedge HE-1 (induced by source paper "strategyproof peer selection using randomization partitioning and apportionment"):
  with members:
    - "friend based ranking"
    - "the conference paper assignment problem using order weighted averages to assign indivisible goods"
    - "design and analysis of the nips 2016 review process"
    - "aggregating partial rankings with applications to peer grading in massive online open courses"
    - "a near optimal mechanism for impartial selection"
    - "the peerrank method for peer assessment"
    - "crowdgrader crowdsourcing the evaluation of homework assignments"
    - "incentives quality and risks a look into the nsf proposal review pilot"

Connected through hyperedge HE-2 (induced by source paper "optimizing positional scoring rules for rank aggregation"):
  with members:
    - "simple robust and optimal ranking from pairwise comparisons"
    - "aggregating partial rankings with applications to peer grading in massive online open courses"
    - "methods for ordinal peer grading"
    - "random utility theory for social choice"
    - "common voting rules as maximum likelihood estimators"
    - "determining possible and necessary winners under common voting rules given partial orders"
    - "voting with limited information and many alternatives"
    - "noisy sorting without resampling"

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)