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: "aggregating partial rankings with applications to peer grading in massive online open courses"
  Abstract: We investigate the potential of using ordinal peer grading for the evaluation of students in massive online open courses (MOOCs). According to such grading schemes, each student receives a few assignments (by other students) which she has to rank. Then, a global ranking (possibly translated into numerical scores) is produced by combining the individual ones. This is a novel application area for social choice concepts and methods where the important problem to be solved is as follows: how should the assignments be distributed so that the collected individual rankings can be easily merged into a global one that is as close as possible to the ranking that represents the relative performance of the students in the assignment? Our main theoretical result suggests that using very simple ways to distribute the assignments so that each student has to rank only $k$ of them, a Borda-like aggregation method can recover a $1-O(1/k)$ fraction of the true ranking when each student correctly ranks the assignments she receives. Experimental results strengthen our analysis further and also demonstrate that the same method is extremely robust even when students have imperfect capabilities as graders. We believe that our results provide strong evidence that ordinal peer grading can be a highly effective and scalable solution for evaluation in MOOCs.

Connected through hyperedge HE-1 (induced by source paper "incentivizing effort and precision in peer grading"):
  with members:
    - "incentivizing evaluation via limited access to ground truth peer prediction makes things worse"
    - "informed truthfulness in multi task peer prediction"
    - "methods for ordinal peer grading"
    - "tuned models of peer assessment in moocs"
    - "crowdsourced judgement elicitation with endogenous proficiency"
    - "mechanisms for making crowds truthful"

Connected through hyperedge HE-2 (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"
    - "how effective can simple ordinal peer grading be"
    - "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-3 (induced by source paper "optimizing positional scoring rules for rank aggregation"):
  with members:
    - "how effective can simple ordinal peer grading be"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "strategyproof peer selection"):
  with members:
    - "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"
    - "tuned models of peer assessment in moocs"
    - "optimal impartial selection"
    - "sum of us strategyproof selection from the selectors"

Connected through hyperedge HE-5 (induced by source paper "how effective can simple ordinal peer grading be"):
  with members:
    - "the peerrank method for peer assessment"
    - "methods for ordinal peer grading"
    - "tuned models of peer assessment in moocs"
    - "random utility theory for social choice"
    - "common voting rules as maximum likelihood estimators"
    - "voting with limited information and many alternatives"

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)