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: "tradeoffs between information and ordinal approximation for bipartite matching"
  Abstract: We study ordinal approximation algorithms for maximum-weight bipartite matchings. Such algorithms only know the ordinal preferences of the agents/nodes in the graph for their preferred matches, but must compete with fully omniscient algorithms which know the true numerical edge weights (utilities). %instead of only their relative orderings. Ordinal approximation is all about being able to produce good results with only limited information. Because of this, one important question is how much better the algorithms can be as the amount of information increases. To address this question for forming high-utility matchings between agents in $\mathcal{X}$ and $\mathcal{Y}$, we consider three ordinal information types: when we know the preference order of only nodes in $\mathcal{X}$ for nodes in $\mathcal{Y}$, when we know the preferences of both $\mathcal{X}$ and $\mathcal{Y}$, and when we know the total order of the edge weights in the entire graph, although not the weights themselves. We also consider settings where only the top preferences of the agents are known to us, instead of their full preference orderings. We design new ordinal approximation algorithms for each of these settings, and quantify how well such algorithms perform as the amount of information given to them increases.

Connected through hyperedge HE-1 (induced by source paper "peeking behind the ordinal curtain improving distortion via cardinal queries"):
  with members:
    - "improved metric distortion for deterministic social choice rules"
    - "the distortion of distributed voting"
    - "awareness of voter passion greatly improves the distortion of metric social choice"
    - "approval based elections and distortion of voting rules"
    - "an analysis framework for metric voting based on lp duality"
    - "on the distortion value of the elections with abstention"
    - "random dictators with a random referee constant sample complexity mechanisms for social choice"
    - "ordinal approximation for social choice matching and facility location problems given candidate positions"

Connected through hyperedge HE-2 (induced by source paper "improved metric distortion for deterministic social choice rules"):
  with members:
    - "ordinal approximation for social choice matching and facility location problems given candidate positions"
    - "relating metric distortion and fairness of social choice rules"
    - "of the people voting is more effective with representative candidates"
    - "on the distortion of voting with multiple representative candidates"
    - "metric distortion of social choice rules lower bounds and fairness properties"
    - "randomized social choice functions under metric preferences"
    - "on voting and facility location"

Connected through hyperedge HE-3 (induced by source paper "ordinal approximation for social choice matching and facility location problems given candidate positions"):
  with members:
    - "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"
    - "truthful mechanisms for matching and clustering in an ordinal world"
    - "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"

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)