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: "vote elicitation complexity and strategy proofness"
  Abstract: Preference elicitation is a central problem in AI, and has received significant attention in single-agent settings. It is also a key problem in multiagent systems, but has received little attention here so far. In this setting, the agents may have different preferences that often must be aggregated using voting. This leads to interesting issues because what, if any, information should be elicited from an agent depends on what other agents have revealed about their preferences so far. #R##N#In this paper we study effective elicitation, and its impediments, for the most common voting protocols. It turns out that in the Single Transferable Vote protocol, even knowing when to terminate elicitation is mathcal NP-complete, while this is easy for all the other protocols under study. Even for these protocols, determining how to elicit effectively is NP-complete, even with perfect suspicions about how the agents will vote. The exception is the Plurality protocol where such effective elicitation is easy. #R##N#We also show that elicitation introduces additional opportunities for strategic manipulation by the voters. We demonstrate how to curtail the space of elicitation schemes so that no such additional strategic issues arise.

Connected through hyperedge HE-1 (induced by source paper "communication distortion and randomness in metric voting"):
  with members:
    - "comparing election methods where each voter ranks only few candidates"
    - "improved metric distortion for deterministic social choice rules"
    - "approval based elections and distortion of voting rules"
    - "an analysis framework for metric voting based on lp duality"
    - "random dictators with a random referee constant sample complexity mechanisms for social choice"
    - "of the people voting is more effective with representative candidates"
    - "on the distortion of voting with multiple representative candidates"
    - "combinatorial auctions do need modest interaction"

Connected through hyperedge HE-2 (induced by source paper "minimizing time to rank a learning and recommendation approach"):
  with members:
    - "composite marginal likelihood methods for random utility models"
    - "learning mixtures of plackett luce models"
    - "learning mixtures of ranking models"
    - "preference elicitation for general random utility models"
    - "noisy sorting without resampling"

Connected through hyperedge HE-3 (induced by source paper "distributed monitoring of election winners"):
  with members:
    - "comparing election methods where each voter ranks only few candidates"
    - "optimal random sampling from distributed streams revisited"
    - "proportional representation in vote streams"
    - "optimizing positional scoring rules for rank aggregation"
    - "fishing out winners from vote streams"
    - "estimating the margin of victory of an election using sampling"
    - "sample complexity for winner prediction in elections"
    - "crowdsourcing for participatory democracies efficient elicitation of social choice functions"

Connected through hyperedge HE-4 (induced by source paper "proportional representation in vote streams"):
  with members:
    - "distributed monitoring of election winners"
    - "an optimal algorithm for l1 heavy hitters in insertion streams and related problems"
    - "better streaming algorithms for the maximum coverage problem"
    - "fishing out winners from vote streams"
    - "sample complexity for winner prediction in elections"
    - "properties of multiwinner voting rules"
    - "finding a collective set of items from proportional multirepresentation to group recommendation"
    - "on the computation of fully proportional representation"

Connected through hyperedge HE-5 (induced by source paper "preference elicitation for single crossing domain"):
  with members:
    - "elicitation for preferences single peaked on trees"
    - "the complexity of fully proportional representation for single crossing electorates"
    - "the complexity of manipulative attacks in nearly single peaked electorates"
    - "the shield that never was societies with single peaked preferences are more open to manipulation and control"
    - "eliciting single peaked preferences using comparison queries"

Connected through hyperedge HE-6 (induced by source paper "elicitation for preferences single peaked on trees"):
  with members:
    - "preference elicitation for single crossing domain"
    - "eliciting single peaked preferences using comparison queries"

Connected through hyperedge HE-7 (induced by source paper "aggregating partial rankings with applications to peer grading in massive online open courses"):
  with members:
    - "the peerrank method for peer assessment"
    - "methods for ordinal peer grading"
    - "preference elicitation for general random utility models"
    - "tuned models of peer assessment in moocs"
    - "determining possible and necessary winners under common voting rules given partial orders"
    - "voting with limited information and many alternatives"
    - "sum of us strategyproof selection from the selectors"

Connected through hyperedge HE-8 (induced by source paper "crowdsourcing for participatory democracies efficient elicitation of social choice functions"):
  with members:
    - "triadic consensus a randomized algorithm for voting in a crowd"
    - "eliciting single peaked preferences using comparison queries"

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)