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: "truthful mechanisms for matching and clustering in an ordinal world"
  Abstract: We study truthful mechanisms for matching and related problems in a partial information setting, where the agents' true utilities are hidden, and the algorithm only has access to ordinal preference information. Our model is motivated by the fact that in many settings, agents cannot express the numerical values of their utility for different outcomes, but are still able to rank the outcomes in their order of preference. Specifically, we study problems where the ground truth exists in the form of a weighted graph of agent utilities, but the algorithm can only elicit the agents' private information in the form of a preference ordering for each agent induced by the underlying weights. Against this backdrop, we design truthful algorithms to approximate the true optimum solution with respect to the hidden weights. Our techniques yield universally truthful algorithms for a number of graph problems: a 1.76-approximation algorithm for Max-Weight Matching, 2-approximation algorithm for Max k-matching, a 6-approximation algorithm for Densest k-subgraph, and a 2-approximation algorithm for Max Traveling Salesman as long as the hidden weights constitute a metric. We also provide improved approximation algorithms for such problems when the agents are not able to lie about their preferences. Our results are the first non-trivial truthful approximation algorithms for these problems, and indicate that in many situations, we can design robust algorithms even when the agents may lie and only provide ordinal information instead of precise utilities.

Connected through hyperedge HE-1 (induced by source paper "ordinal approximation for social choice matching and facility location problems given candidate positions"):
  with members:
    - "tradeoffs between information and ordinal approximation for bipartite matching"
    - "sequential deliberation for social choice"
    - "of the people voting is more effective with representative candidates"
    - "on the distortion of voting with multiple representative candidates"
    - "combinatorial secretary problems with ordinal information"
    - "metric distortion of social choice rules lower bounds and fairness properties"
    - "truthful facility assignment with resource augmentation an exact analysis of serial dictatorship"
    - "randomized social choice functions under metric preferences"

Connected through hyperedge HE-2 (induced by source paper "tradeoffs between information and ordinal approximation for bipartite matching"):
  with members:
    - "size versus truthfulness in the house allocation problem"
    - "metric distortion of social choice rules lower bounds and fairness properties"
    - "truthful facility assignment with resource augmentation an exact analysis of serial dictatorship"
    - "on voting and facility location"
    - "social welfare in one sided matching mechanisms"
    - "social welfare in one sided matchings random priority and beyond"
    - "welfare maximization and truthfulness in mechanism design with ordinal preferences"
    - "truthful approximations to range voting"

Connected through hyperedge HE-3 (induced by source paper "utilitarians without utilities maximizing social welfare for graph problems using only ordinal preferences full version"):
  with members:
    - "metric distortion of social choice rules lower bounds and fairness properties"
    - "randomized social choice functions under metric preferences"
    - "a 7 9 approximation algorithm for the maximum traveling salesman problem"

Connected through hyperedge HE-4 (induced by source paper "metric distortion of social choice rules lower bounds and fairness properties"):
  with members:
    - "randomized social choice functions under metric preferences"
    - "on voting and facility location"
    - "truthful approximations to range voting"

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)