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: "beyond equilibria mechanisms for repeated combinatorial auctions"
  Abstract: We study the design of mechanisms in combinatorial auction domains. We focus on settings where the auction is repeated, motivated by auctions for licenses or advertising space. We consider models of agent behaviour in which they either apply common learning techniques to minimize the regret of their bidding strategies, or apply short-sighted best-response strategies. We ask: when can a black-box approximation algorithm for the base auction problem be converted into a mechanism that approximately preserves the original algorithm's approximation factor on average over many iterations? We present a general reduction for a broad class of algorithms when agents minimize external regret. We also present a new mechanism for the combinatorial auction problem that attains an $O(\sqrt{m})$ approximation on average when agents apply best-response dynamics.

Connected through hyperedge HE-1 (induced by source paper "a general framework for evaluating callout mechanisms in repeated auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "learning prices for repeated auctions with strategic buyers"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"
    - "selective call out and real time bidding"

Connected through hyperedge HE-2 (induced by source paper "simple mechanisms for agents with complements"):
  with members:
    - "simple auctions for agents with complements"
    - "interpolating between truthful and non truthful mechanisms for combinatorial auctions"
    - "the pseudo dimension of near optimal auctions"
    - "greedy algorithms make efficient mechanisms"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"
    - "on the complexity of computing an equilibrium in combinatorial auctions"
    - "a simple and approximately optimal mechanism for an additive buyer"
    - "an n to 1 bidder reduction for multi item auctions and its applications"

Connected through hyperedge HE-3 (induced by source paper "bounding the inefficiency of outcomes in generalized second price auctions"):
  with members:
    - "bayesian games and the smoothness framework"
    - "composable and efficient mechanisms"
    - "improved social welfare bounds for gsp at equilibrium"
    - "price of anarchy for greedy auctions"

Connected through hyperedge HE-4 (induced by source paper "improved social welfare bounds for gsp at equilibrium"):
  with members:
    - "price of anarchy for greedy auctions"

Connected through hyperedge HE-5 (induced by source paper "price of anarchy for greedy auctions"):
  with members:
    - "an impossibility result for truthful combinatorial auctions with submodular valuations"
    - "mechanisms for multi unit auctions"
    - "truthful mechanisms via greedy iterative packing"
    - "combinatorial auctions with decreasing marginal utilities"
    - "optimal solutions for multi unit combinatorial auctions branch and bound heuristics"

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)