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: "enhancing engagement in token curated registries via an inflationary mechanism"
  Abstract: Token Curated Registries (TCR) are decentralized recommendation systems that can be implemented using Blockchain smart contracts. They allow participants to vote for or against adding items to a list through a process that involves staking tokens intrinsic to the registry, with winners receiving the staked tokens for each vote. A TCR aims to provide incentives to create a well-curated list. In this work, we consider a challenge for these systems - incentivizing token-holders to actually engage and participate in the voting process. We propose a novel token-inflation mechanism for enhancing engagement, whereby only voting participants see their token supply increased by a pre-defined multiple after each round of voting. To evaluate this proposal, we propose a simple 4-class model of voters that captures all possible combinations of two key dimensions: whether they are engaged (likely to vote at all for a given item) or disengaged, and whether they are informed (likely to vote in a way that increases the quality of the list) or uninformed, and a simple metric to evaluate the quality of the list as a function of the vote outcomes. We conduct simulations using this model of voters and show that implementing token-inflation results in greater wealth accumulation for engaged voters. In particular, when the number of informed voters is sufficiently high, our simulations show that voters that are both informed and engaged see the greatest benefits from participating in the registry when our proposed token-inflation mechanism is employed. We further validate this finding using a simplified mathematical analysis.

Connected through hyperedge HE-1 (induced by source paper "token curated registry with citation graph"):
  with members:
    - "token curated registries a game theoretic approach"
    - "a memo on the proof of stake mechanism"
    - "peer prediction with heterogeneous tasks"
    - "informed truthfulness in multi task peer prediction"
    - "augur a decentralized open source platform for prediction markets"
    - "crowdsourced judgement elicitation with endogenous proficiency"
    - "recommendation on academic networks using direction aware citation analysis"

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)