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: "strategyproof linear regression in high dimensions"
  Abstract: This paper is part of an emerging line of work at the intersection of machine learning and mechanism design, which aims to avoid noise in training data by correctly aligning the incentives of data sources. Specifically, we focus on the ubiquitous problem of linear regression, where strategyproof mechanisms have previously been identified in two dimensions. In our setting, agents have single-peaked preferences and can manipulate only their response variables. Our main contribution is the discovery of a family of group strategyproof linear regression mechanisms in any number of dimensions, which we call generalized resistant hyperplane mechanisms. The game-theoretic properties of these mechanisms -- and, in fact, their very existence -- are established through a connection to a discrete version of the Ham Sandwich Theorem.

Connected through hyperedge HE-1 (induced by source paper "mechanism design for multi party machine learning"):
  with members:
    - "incentive design for efficient federated learning in mobile networks a contract theory approach"
    - "federated learning strategies for improving communication efficiency"
    - "privacy preserving visual learning using doubly permuted homomorphic encryption"
    - "communication efficient learning of deep networks from decentralized data"
    - "truthful linear regression"
    - "auctions with severely bounded communication"

Connected through hyperedge HE-2 (induced by source paper "grinding the space learning to classify against strategic agents"):
  with members:
    - "equal opportunity in online classification with partial feedback"
    - "distribution independent pac learning of halfspaces with massart noise"
    - "strategizing against no regret learners"
    - "introduction to multi armed bandits"
    - "nearly tight bounds for robust proper learning of halfspaces with a margin"
    - "competing prediction algorithms"
    - "strategic classification from revealed preferences"
    - "an improved parametrization and analysis of the exp3 algorithm for stochastic and adversarial bandits"

Connected through hyperedge HE-3 (induced by source paper "prior free data acquisition for accurate statistical estimation"):
  with members:
    - "strategic classification from revealed preferences"
    - "optimal data acquisition for statistical estimation"
    - "learning to incentivize eliciting effort via output agreement"
    - "informed truthfulness in multi task peer prediction"
    - "sequential peer prediction learning to elicit effort using posted prices"
    - "low cost learning via active data procurement"
    - "truthful linear regression"
    - "redrawing the boundaries on purchasing data from privacy sensitive individuals"

Connected through hyperedge HE-4 (induced by source paper "rewarding high quality data via influence functions"):
  with members:
    - "competitive statistical estimation with strategic data sources"
    - "towards efficient data valuation based on the shapley value"
    - "federated learning strategies for improving communication efficiency"
    - "optimal data acquisition for statistical estimation"
    - "statistical estimation with strategic data sources in competitive settings"
    - "truthful linear regression"
    - "approval voting and incentives in crowdsourcing"

Connected through hyperedge HE-5 (induced by source paper "incentive compatible active learning"):
  with members:
    - "learning time dependent choice"
    - "low cost learning via active data procurement"
    - "learning economic parameters from revealed preferences"
    - "efficiently learning from revealed preference"
    - "prediction markets mechanism design and cooperative game theory"

Connected through hyperedge HE-6 (induced by source paper "impartial selection with additive approximation guarantees"):
  with members:
    - "incentive compatible diffusion"
    - "a near optimal mechanism for impartial selection"
    - "optimal impartial selection"
    - "sum of us strategyproof selection from the selectors"

Connected through hyperedge HE-7 (induced by source paper "how do classifiers induce agents to invest effort strategically"):
  with members:
    - "the disparate effects of strategic manipulation"
    - "a short term intervention for long term fairness in the labor market"
    - "the social cost of strategic classification"
    - "strategic classification from revealed preferences"
    - "truthful linear regression"

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)