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 "a learning framework for distribution based game theoretic solution concepts"):
  Abstract: The past few years have seen several works establishing PAC frameworks for solving various problems in economic domains; these include optimal auction design, approximate optima of submodular functions, stable partitions and payoff divisions in cooperative games and more. In this work, we provide a unified learning-theoretic methodology for modeling these problems, and establish some useful tools for determining whether a given economic solution concept can be learned from data. Our learning theoretic framework generalizes a notion of function space dimension --- the graph dimension --- adapting it to the solution concept learning domain. We identify sufficient conditions for the PAC learnability of solution concepts, and show that results in existing works can be immediately derived using our general methodology. Finally, we apply our methods in other economic domains, yielding a novel notion of PAC competitive equilibrium and PAC Condorcet winners.

Includes members:
  Member "forming probably stable communities with limited interactions":
    also connected through other hyperedges:
      (none)
  Member "envy free classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "preference informed fairness")
  Member "statistical cost sharing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a marketplace for data an algorithmic solution")
  Member "a sample complexity measure with applications to learning optimal auctions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "hedonic games with graph restricted communication":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hedonic seat arrangement problems")
  Member "learning simple auctions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "sample complexity of automated mechanism design":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning reserve prices in second price auctions")
  Member "learning cooperative games":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robustness against agent failure in hedonic games")

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.AI(Artificial Intelligence)