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: "properties of multiwinner voting rules"
  Abstract: The goal of this paper is to propose and study properties of multiwinner voting rules which can be consider as generalisations of single-winner scoring voting rules. We consider SNTV, Bloc, k-Borda, STV, and several variants of Chamberlin--Courant's and Monroe's rules and their approximations. We identify two broad natural classes of multiwinner score-based rules, and show that many of the existing rules can be captured by one or both of these approaches. We then formulate a number of desirable properties of multiwinner rules, and evaluate the rules we consider with respect to these properties.

Connected through hyperedge HE-1 (induced by source paper "who is in your top three optimizing learning in elections with many candidates"):
  with members:
    - "truthful aggregation of budget proposals"
    - "a framework for approval based budgeting methods"
    - "designing optimal binary rating systems"
    - "a quantitative analysis of multi winner rules"
    - "designing informative rating systems for online platforms evidence from two experiments"
    - "consistent approval based multi winner rules"
    - "iterative local voting for collective decision making in continuous spaces"
    - "optimizing positional scoring rules for rank aggregation"

Connected through hyperedge HE-2 (induced by source paper "proportionally representative participatory budgeting with ordinal preferences"):
  with members:
    - "a framework for approval based budgeting methods"
    - "fair allocation of indivisible public goods"
    - "the condorcet principle for multiwinner elections from shortlisting to proportionality"
    - "proportionally representative participatory budgeting axioms and algorithms"
    - "fair mixing the case of dichotomous preferences"
    - "rank maximal equal contribution a probabilistic social choice function"
    - "the expanding approvals rule improving proportional representation and monotonicity"
    - "the core of the participatory budgeting problem"

Connected through hyperedge HE-3 (induced by source paper "unifying ensemble methods for q learning via social choice theory"):
  with members:
    - "multi step reinforcement learning a unifying algorithm"
    - "the condorcet principle for multiwinner elections from shortlisting to proportionality"
    - "averaged dqn variance reduction and stabilization for deep reinforcement learning"
    - "deep exploration via bootstrapped dqn"
    - "epopt learning robust neural network policies using model ensembles"
    - "dueling network architectures for deep reinforcement learning"
    - "off policy shaping ensembles in reinforcement learning"
    - "justified representation in approval based committee voting"

Connected through hyperedge HE-4 (induced by source paper "approximately stable committee selection"):
  with members:
    - "proportionally fair clustering"
    - "group fairness in committee selection"
    - "fair knapsack"
    - "fair allocation of indivisible public goods"
    - "proportionally representative participatory budgeting axioms and algorithms"
    - "the expanding approvals rule improving proportional representation and monotonicity"
    - "the core of the participatory budgeting problem"
    - "an improved approximation for k median and positive correlation in budgeted optimization"

Connected through hyperedge HE-5 (induced by source paper "modelling bahdanau attention using election methods aided by q learning"):
  with members:
    - "equality of voice towards fair representation in crowdsourced top k recommendations"
    - "attention is all you need"
    - "massive exploration of neural machine translation architectures"
    - "word translation without parallel data"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-6 (induced by source paper "electing a committee with dominance constraints"):
  with members:
    - "a rule for committee selection with soft diversity constraints"
    - "multiwinner elections with diversity constraints"
    - "multiwinner voting with fairness constraints"
    - "multi attribute proportional representation"
    - "finding a collective set of items from proportional multirepresentation to group recommendation"
    - "on the computation of fully proportional representation"

Connected through hyperedge HE-7 (induced by source paper "what do multiwinner voting rules do an experiment over the two dimensional euclidean domain"):
  with members:
    - "committee scoring rules axiomatic characterization and hierarchy"
    - "consistent approval based multi winner rules"
    - "finding a collective set of items from proportional multirepresentation to group recommendation"
    - "on the computation of fully proportional representation"
    - "cloning in elections finding the possible winners"

Connected through hyperedge HE-8 (induced by source paper "group fairness in committee selection"):
  with members:
    - "fair allocation of indivisible public goods"
    - "the core of the participatory budgeting problem"
    - "proportional justified representation"
    - "justified representation in approval based committee voting"

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)