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: "computing large market equilibria using abstractions"
  Abstract: Computing market equilibria is an important practical problem for market design (e.g. fair division, item allocation). However, computing equilibria requires large amounts of information (e.g. all valuations for all buyers for all items) and compute power. We consider ameliorating these issues by applying a method used for solving complex games: constructing a coarsened abstraction of a given market, solving for the equilibrium in the abstraction, and lifting the prices and allocations back to the original market. We show how to bound important quantities such as regret, envy, Nash social welfare, Pareto optimality, and maximin share when the abstracted prices and allocations are used in place of the real equilibrium. We then study two abstraction methods of interest for practitioners: 1) filling in unknown valuations using techniques from matrix completion, 2) reducing the problem size by aggregating groups of buyers/items into smaller numbers of representative buyers/items and solving for equilibrium in this coarsened market. We find that in real data allocations/prices that are relatively close to equilibria can be computed from even very coarse abstractions.

Connected through hyperedge HE-1 (induced by source paper "increasing iterate averaging for solving saddle point problems"):
  with members:
    - "optimistic regret minimization for extensive form games via dilated distance generating functions"
    - "solving large sequential games with the excessive gap technique"
    - "deepstack expert level artificial intelligence in heads up no limit poker"

Connected through hyperedge HE-2 (induced by source paper "fair division without disparate impact"):
  with members:
    - "pacing equilibrium in first price auction markets"
    - "beyond parity fairness objectives for collaborative filtering"
    - "algorithmic decision making and the cost of fairness"
    - "convex program duality fisher markets and nash social welfare"
    - "on the im possibility of fairness"
    - "inherent trade offs in the fair determination of risk scores"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "fairness through awareness"

Connected through hyperedge HE-3 (induced by source paper "robust market equilibria with uncertain preferences"):
  with members:
    - "fair division without disparate impact"
    - "robust multi agent counterfactual prediction"
    - "pacing equilibrium in first price auction markets"
    - "multiplicative pacing equilibria in auction markets"
    - "convex program duality fisher markets and nash social welfare"
    - "market equilibrium with transaction costs"

Connected through hyperedge HE-4 (induced by source paper "scalable fair division for at most one preferences"):
  with members:
    - "fair division without disparate impact"
    - "pacing equilibrium in first price auction markets"
    - "convex program duality fisher markets and nash social welfare"
    - "the complexity of fairness through equilibrium"

Connected through hyperedge HE-5 (induced by source paper "first order methods with increasing iterate averaging for solving saddle point problems"):
  with members:
    - "solving large sequential games with the excessive gap technique"
    - "online convex optimization for sequential decision processes and extensive form games"
    - "pacing equilibrium in first price auction markets"
    - "multiplicative pacing equilibria in auction markets"

Connected through hyperedge HE-6 (induced by source paper "pacing equilibrium in first price auction markets"):
  with members:
    - "fair division without disparate impact"
    - "multiplicative pacing equilibria in auction markets"
    - "convex program duality fisher markets and nash social welfare"
    - "on revenue maximization in second price ad auctions"
    - "budget optimization in search based advertising auctions"

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)