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: "crowdgrader crowdsourcing the evaluation of homework assignments"
  Abstract: Crowdsourcing offers a practical method for ranking and scoring large amounts of items. To investigate the algorithms and incentives that can be used in crowdsourcing quality evaluations, we built CrowdGrader, a tool that lets students submit and collaboratively grade solutions to homework assignments. We present the algorithms and techniques used in CrowdGrader, and we describe our results and experience in using the tool for several computer-science assignments. #R##N#CrowdGrader combines the student-provided grades into a consensus grade for each submission using a novel crowdsourcing algorithm that relies on a reputation system. The algorithm iterativerly refines inter-dependent estimates of the consensus grades, and of the grading accuracy of each student. On synthetic data, the algorithm performs better than alternatives not based on reputation. On our preliminary experimental data, the performance seems dependent on the nature of review errors, with errors that can be ascribed to the reviewer being more tractable than those arising from random external events. To provide an incentive for reviewers, the grade each student receives in an assignment is a combination of the consensus grade received by their submissions, and of a reviewing grade capturing their reviewing effort and accuracy. This incentive worked well in practice.

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"
    - "how effective can simple ordinal peer grading be"
    - "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"
    - "incentives quality and risks a look into the nsf proposal review pilot"

Connected through hyperedge HE-2 (induced by source paper "strategyproof peer selection"):
  with members:
    - "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"
    - "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-3 (induced by source paper "the peerrank method for peer assessment"):
  with members:
    - "incentives quality and risks a look into the nsf proposal review pilot"
    - "tuned models of peer assessment in moocs"
    - "sum of us strategyproof selection from the selectors"

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.SI(Social and Information Networks)