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: "preference elicitation for single crossing domain"
  Abstract: Eliciting the preferences of a set of agents over a set of alternatives is a problem of fundamental importance in social choice theory. Prior work on this problem has studied the query complexity of preference elicitation for the unrestricted domain and for the domain of single peaked preferences. In this paper, we consider the domain of single crossing preference profiles and study the query complexity of preference elicitation under various settings. We consider two distinct situations: when an ordering of the voters with respect to which the profile is single crossing is known versus when it is unknown. We also consider different access models: when the votes can be accessed at random, as opposed to when they are coming in a pre-defined sequence. In the sequential access model, we distinguish two cases when the ordering is known: the first is that sequence in which the votes appear is also a single-crossing order, versus when it is not. #R##N#The main contribution of our work is to provide polynomial time algorithms with low query complexity for preference elicitation in all the above six cases. Further, we show that the query complexities of our algorithms are optimal up to constant factors for all but one of the above six cases. We then present preference elicitation algorithms for profiles which are close to being single crossing under various notions of closeness, for example, single crossing width, minimum number of candidates | voters whose deletion makes a profile single crossing.

Connected through hyperedge HE-1 (induced by source paper "testing preferential domains using sampling"):
  with members:
    - "computational aspects of nearly single peaked electorates"
    - "recognising multidimensional euclidean preferences"
    - "single peaked consistency for weak orders is easy"
    - "clone structures in voters preferences"

Connected through hyperedge HE-2 (induced by source paper "manipulative elicitation a new attack on elections with incomplete preferences"):
  with members:
    - "on the exact amount of missing information that makes finding possible winners hard"
    - "elicitation for preferences single peaked on trees"
    - "query complexity of tournament solutions"
    - "complexity of manipulation with partial information in voting"
    - "estimating the margin of victory of an election using sampling"
    - "frugal bribery in voting"
    - "detecting possible manipulators in elections"
    - "kernelization complexity of possible winner and coalitional manipulation problems in voting"

Connected through hyperedge HE-3 (induced by source paper "recognizing and eliciting weakly single crossing profiles on trees"):
  with members:
    - "computational aspects of nearly single peaked electorates"
    - "elicitation for preferences single peaked on trees"
    - "structure in dichotomous preferences"
    - "the single crossing property on a tree"
    - "on the computation of fully proportional representation"
    - "the complexity of fully proportional representation for single crossing electorates"
    - "the complexity of manipulative attacks in nearly single peaked electorates"
    - "eliciting single peaked preferences using comparison queries"

Connected through hyperedge HE-4 (induced by source paper "elicitation for preferences single peaked on trees"):
  with members:
    - "eliciting single peaked preferences using comparison queries"
    - "vote elicitation complexity and strategy proofness"

Connected through hyperedge HE-5 (induced by source paper "query complexity of tournament solutions"):
  with members:
    - "elicitation for preferences single peaked on trees"
    - "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)