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: "best response dynamics in combinatorial auctions with item bidding"
  Abstract: In a combinatorial auction with item bidding, agents participate in multiple single-item second-price auctions at once. As some items might be substitutes, agents need to strategize in order to maximize their utilities. A number of results indicate that high welfare can be achieved this way, giving bounds on the welfare at equilibrium. Recently, however, criticism has been raised that equilibria are hard to compute and therefore unlikely to be attained. #R##N#In this paper, we take a different perspective. We study simple best-response dynamics. That is, agents are activated one after the other and each activated agent updates his strategy myopically to a best response against the other agents' current strategies. Often these dynamics may take exponentially long before they converge or they may not converge at all. However, as we show, convergence is not even necessary for good welfare guarantees. Given that agents' bid updates are aggressive enough but not too aggressive, the game will remain in states of good welfare after each agent has updated his bid at least once. #R##N#In more detail, we show that if agents have fractionally subadditive valuations, natural dynamics reach and remain in a state that provides a $1/3$ approximation to the optimal welfare after each agent has updated his bid at least once. For subadditive valuations, we can guarantee an $\Omega(1/\log m)$ approximation in case of $m$ items that applies after each agent has updated his bid at least once and at any point after that. The latter bound is complemented by a negative result, showing that no kind of best-response dynamics can guarantee more than an $o(\log \log m/\log m)$ fraction of the optimal social welfare.

Connected through hyperedge HE-1 (induced by source paper "proportional dynamics in exchange economies"):
  with members:
    - "an alternating algorithm for finding linear arrow debreu market equilibrium"
    - "universal growth in production economies"
    - "a strongly polynomial algorithm for linear exchange markets"
    - "tracing equilibrium in dynamic markets via distributed adaptation"
    - "dynamics of distributed updating in fisher markets"
    - "selling complementary goods dynamics efficiency and revenue"
    - "the price of anarchy in auctions"
    - "ascending price algorithms for unknown markets"

Connected through hyperedge HE-2 (induced by source paper "pure nash equilibria and best response dynamics in random games"):
  with members:
    - "flows and decompositions of games harmonic and potential games"
    - "on the structure of weakly acyclic games"

Connected through hyperedge HE-3 (induced by source paper "universal growth in production economies"):
  with members:
    - "selling complementary goods dynamics efficiency and revenue"
    - "nash social welfare approximation for strategic agents"
    - "the price of anarchy in auctions"
    - "learning and efficiency in games with dynamic population"
    - "learning in auctions regret is hard envy is easy"
    - "average case performance of replicator dynamics in potential games via computing regions of attraction"
    - "concave generalized flows with applications to market equilibria"
    - "non price equilibria in markets of discrete goods"

Connected through hyperedge HE-4 (induced by source paper "a bridge between liquid and social welfare in combinatorial auctions with submodular bidders"):
  with members:
    - "a bayesian clearing mechanism for combinatorial auctions"
    - "prophet inequalities made easy stochastic optimization by pricing non stochastic inputs"
    - "breaking the logarithmic barrier for truthful combinatorial auctions with submodular bidders"
    - "bayesian budget feasibility with posted pricing"
    - "combinatorial auctions via posted prices"
    - "budget feasible mechanism design from prior free to bayesian"
    - "from convex optimization to randomized mechanisms toward optimal combinatorial auctions"
    - "on the approximability of budget feasible mechanisms"

Connected through hyperedge HE-5 (induced by source paper "combinatorial auctions do need modest interaction"):
  with members:
    - "on estimating maximum matching size in graph streams"
    - "on simultaneous two player combinatorial auctions"
    - "computational efficiency requires simple taxation"
    - "welfare maximization with limited interaction"
    - "combinatorial auctions via posted prices"
    - "economic efficiency requires interaction"
    - "simultaneous auctions are almost efficient"
    - "learning valuation functions"

Connected through hyperedge HE-6 (induced by source paper "walrasian dynamics in multi unit markets"):
  with members:
    - "optimal multi unit mechanisms with private demands"
    - "when does the price of anarchy tend to 1 in large walrasian auctions and fisher markets"
    - "the menu size complexity of auctions"
    - "non price equilibria in markets of discrete goods"
    - "on nash dynamics of matching market equilibria"
    - "multi unit auctions beyond roberts"
    - "price of anarchy for greedy auctions"

Connected through hyperedge HE-7 (induced by source paper "the price of anarchy in auctions"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "simple mechanisms for agents with complements"
    - "the price of anarchy in large games"
    - "on the economic efficiency of the combinatorial clock auction"
    - "on the efficiency of all pay mechanisms"
    - "learning and efficiency in games with dynamic population"
    - "no regret learning in bayesian games"
    - "communication complexity for algorithm designers"

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)