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: "sum of us strategyproof selection from the selectors"
  Abstract: We consider directed graphs over a set of n agents, where an edge (i,j) is taken to mean that agent i supports or trusts agent j. Given such a graph and an integer k\leq n, we wish to select a subset of k agents that maximizes the sum of indegrees, i.e., a subset of k most popular or most trusted agents. At the same time we assume that each individual agent is only interested in being selected, and may misreport its outgoing edges to this end. This problem formulation captures realistic scenarios where agents choose among themselves, which can be found in the context of Internet search, social networks like Twitter, or reputation systems like Epinions. #R##N#Our goal is to design mechanisms without payments that map each graph to a k-subset of agents to be selected and satisfy the following two constraints: strategyproofness, i.e., agents cannot benefit from misreporting their outgoing edges, and approximate optimality, i.e., the sum of indegrees of the selected subset of agents is always close to optimal. Our first main result is a surprising impossibility: for k \in {1,...,n-1}, no deterministic strategyproof mechanism can provide a finite approximation ratio. Our second main result is a randomized strategyproof mechanism with an approximation ratio that is bounded from above by four for any value of k, and approaches one as k grows.

Connected through hyperedge HE-1 (induced by source paper "impartial selection with additive approximation guarantees"):
  with members:
    - "incentive compatible diffusion"
    - "strategyproof linear regression in high dimensions"
    - "a near optimal mechanism for impartial selection"
    - "optimal impartial selection"

Connected through hyperedge HE-2 (induced by source paper "sequential voting with confirmation network"):
  with members:
    - "optimal impartial selection"
    - "equilibria of plurality voting with abstentions"

Connected through hyperedge HE-3 (induced by source paper "incentive compatible classification"):
  with members:
    - "a near optimal mechanism for impartial selection"
    - "optimal impartial selection"

Connected through hyperedge HE-4 (induced by source paper "on strategyproof conference peer review"):
  with members:
    - "does my rebuttal matter insights from a major nlp conference"
    - "choosing how to choose papers"
    - "design and analysis of the nips 2016 review process"
    - "optimizing positional scoring rules for rank aggregation"
    - "strategyproof peer selection using randomization partitioning and apportionment"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "a near optimal mechanism for impartial selection"
    - "incentive design in peer review rating and repeated endogenous matching"

Connected through hyperedge HE-5 (induced by source paper "friend based ranking"):
  with members:
    - "strategyproof peer selection using randomization partitioning and apportionment"

Connected through hyperedge HE-6 (induced by source paper "incentive compatible diffusion"):
  with members:
    - "optimal impartial selection"

Connected through hyperedge HE-7 (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"
    - "crowdgrader crowdsourcing the evaluation of homework assignments"

Connected through hyperedge HE-8 (induced by source paper "incentives for truthful evaluations"):
  with members:
    - "incentivizing evaluation via limited access to ground truth peer prediction makes things worse"
    - "putting peer prediction under the micro economic scope and making truth telling focal"
    - "truth serums for massively crowdsourced evaluation tasks"
    - "the peerrank method for peer assessment"
    - "methods for ordinal peer grading"
    - "tuned models of peer assessment in moocs"
    - "inducing honest reporting without observing outcomes an application to the peer review process"
    - "mechanisms for making crowds truthful"

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)