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: "common voting rules as maximum likelihood estimators"
  Abstract: Voting is a very general method of preference aggregation. A voting rule takes as input every voter's vote (typically, a ranking of the alternatives), and produces as output either just the winning alternative or a ranking of the alternatives. One potential view of voting is the following. There exists a 'correct' outcome (winner/ranking), and each voter's vote corresponds to a noisy perception of this correct outcome. If we are given the noise model, then for any vector of votes, we can

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"
    - "a maximum likelihood approach for selecting sets of alternatives"

Connected through hyperedge HE-2 (induced by source paper "election coding for distributed learning protecting signsgd against byzantine attacks"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "detox a redundancy based framework for faster and more robust gradient aggregation"
    - "fast and secure distributed learning in high dimension"
    - "byzantine stochastic gradient descent"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "speeding up distributed machine learning using codes"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-3 (induced by source paper "truth discovery via proxy voting"):
  with members:
    - "a survey on truth discovery"
    - "truth discovery algorithms an experimental evaluation"
    - "double or nothing multiplicative incentive mechanisms for crowdsourcing"

Connected through hyperedge HE-4 (induced by source paper "voting with random classifiers vorace"):
  with members:
    - "energy and policy considerations for deep learning in nlp"
    - "xgboost a scalable tree boosting system"
    - "solving multiclass learning problems via error correcting output codes"

Connected through hyperedge HE-5 (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"
    - "a maximum likelihood approach for selecting sets of alternatives"

Connected through hyperedge HE-6 (induced by source paper "robustness among multiwinner voting rules"):
  with members:
    - "what do multiwinner voting rules do an experiment over the two dimensional euclidean domain"
    - "very hard electoral control problems"
    - "distributed monitoring of election winners"
    - "committee scoring rules axiomatic characterization and hierarchy"
    - "algorithms for destructive shift bribery"
    - "the condorcet principle for multiwinner elections from shortlisting to proportionality"
    - "towards computing victory margins in stv elections"
    - "voting and bribing in single exponential time"

Connected through hyperedge HE-7 (induced by source paper "weighted voting via no regret learning"):
  with members:
    - "online ranking with top 1 feedback"
    - "weighted electoral control"
    - "how hard is bribery in elections"

Connected through hyperedge HE-8 (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"
    - "aggregating partial rankings with applications to peer grading in massive online open courses"
    - "methods for ordinal peer grading"
    - "random utility theory for social choice"
    - "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.GT(Computer Science and Game Theory)