human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "size versus truthfulness in the house allocation problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "manipulation strategies for the rank maximal matching problem")
  Member "truthful mechanisms for matching and clustering in an ordinal world":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ordinal approximation for social choice matching and facility location problems given candidate positions")
  Member "metric distortion of social choice rules lower bounds and fairness properties":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "peeking behind the ordinal curtain improving distortion via cardinal queries")
  Member "truthful facility assignment with resource augmentation an exact analysis of serial dictatorship":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "social cost guarantees in smart route guidance")
  Member "on voting and facility location":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "selecting voting locations for fun and profit")
  Member "social welfare in one sided matching mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bounded incentives in manipulating the probabilistic serial rule")
  Member "social welfare in one sided matchings random priority and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "keeping your friends close land allocation with friends")
  Member "welfare maximization and truthfulness in mechanism design with ordinal preferences":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "assortment auctions a myersonian characterization for markov chain based choice models")

Question: Please tell me which class the source paper 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)