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 "smoothness for simultaneous composition of mechanisms with admission"):
  Abstract: We study social welfare of learning outcomes in mechanisms with admission. In our repeated game there are $n$ bidders and $m$ mechanisms, and in each round each mechanism is available for each bidder only with a certain probability. Our scenario is an elementary case of simple mechanism design with incomplete information, where availabilities are bidder types. It captures natural applications in online markets with limited supply and can be used to model access of unreliable channels in wireless networks. #R##N#If mechanisms satisfy a smoothness guarantee, existing results show that learning outcomes recover a significant fraction of the optimal social welfare. These approaches, however, have serious drawbacks in terms of plausibility and computational complexity. Also, the guarantees apply only when availabilities are stochastically independent among bidders. #R##N#In contrast, we propose an alternative approach where each bidder uses a single no-regret learning algorithm and applies it in all rounds. This results in what we call availability-oblivious coarse correlated equilibria. It exponentially decreases the learning burden, simplifies implementation (e.g., as a method for channel access in wireless devices), and thereby addresses some of the concerns about Bayes-Nash equilibria and learning outcomes in Bayesian settings. Our main results are general composition theorems for smooth mechanisms when valuation functions of bidders are lattice-submodular. They rely on an interesting connection to the notion of correlation gap of submodular functions over product lattices.

Includes members:
  Member "learning and efficiency in games with dynamic population":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discovering strategic behaviors for collaborative content production in social networks")
  Member "no regret learning in bayesian games":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discovering strategic behaviors for collaborative content production in social networks")
  Member "learning in auctions regret is hard envy is easy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning reserve prices in second price auctions")
  Member "on the complexity of computing an equilibrium in combinatorial auctions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on simultaneous two player combinatorial auctions")
  Member "jamming resistant learning in wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "effective wireless scheduling via hypergraph sketches")
  Member "tight bounds for the price of anarchy of simultaneous first price auctions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "complexity theory game theory and economics the barbados lectures")
  Member "bounding the inefficiency of outcomes in generalized second price auctions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the tractability of public persuasion with no externalities")
  Member "composable and efficient mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "complexity theory game theory and economics the barbados lectures")

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)