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 "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.

Includes members:
  Member "markets for public decision making":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "participatory budgeting models and approaches")
  Member "deep counterfactual regret minimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "algorithms in multi agent systems a holistic perspective from reinforcement learning and game theory")
  Member "maintaining cooperation in complex social dilemmas using deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "other play for zero shot coordination")
  Member "pacing equilibrium in first price auction markets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stability and learning in strategic queuing systems")
  Member "billion scale similarity search with gpus":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "deepstack expert level artificial intelligence in heads up no limit poker":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium")
  Member "convex program duality fisher markets and nash social welfare":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "maximum nash welfare and other stories about efx")
  Member "imperfect recall abstractions with bounds in games":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "optimistic regret minimization for extensive form games via dilated distance generating functions")

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)