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: "lower bound for envy free and truthful makespan approximation on related machines"
  Abstract: We study problems of scheduling jobs on related machines so as to minimize the makespan in the setting where machines are strategic agents. In this problem, each job $j$ has a length $l_{j}$ and each machine $i$ has a private speed $t_{i}$. The running time of job $j$ on machine $i$ is $t_{i}l_{j}$. We seek a mechanism that obtains speed bids of machines and then assign jobs and payments to machines so that the machines have incentive to report true speeds and the allocation and payments are also envy-free. We show that #R##N#1. A deterministic envy-free, truthful, individually rational, and anonymous mechanism cannot approximate the makespan strictly better than $2-1/m$, where $m$ is the number of machines. This result contrasts with prior work giving a deterministic PTAS for envy-free anonymous assignment and a distinct deterministic PTAS for truthful anonymous mechanism. #R##N#2. For two machines of different speeds, the unique deterministic scalable allocation of any envy-free, truthful, individually rational, and anonymous mechanism is to allocate all jobs to the quickest machine. This allocation is the same as that of the VCG mechanism, yielding a 2-approximation to the minimum makespan. #R##N#3. No payments can make any of the prior published monotone and locally efficient allocations that yield better than an $m$-approximation for $\qcmax$ \cite{aas, at,ck10, dddr, kovacs} a truthful, envy-free, individually rational, and anonymous mechanism.

Connected through hyperedge HE-1 (induced by source paper "envy regret and social welfare loss"):
  with members:
    - "the ad types problem"
    - "online learning for measuring incentive compatibility in ad auctions"
    - "note on vcg vs price raising for matching markets"
    - "communication complexity of approximate nash equilibria"
    - "the price of anarchy in auctions"
    - "setting lower bounds on truthfulness"
    - "learning in auctions regret is hard envy is easy"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "clearing markets via bundles"):
  with members:
    - "combinatorial walrasian equilibrium"
    - "optimization with demand oracles"
    - "an impossibility result for truthful combinatorial auctions with submodular valuations"
    - "envy multi envy and revenue maximization"
    - "bundling equilibrium in combinatorial auctions"

Connected through hyperedge HE-3 (induced by source paper "combinatorial walrasian equilibrium"):
  with members:
    - "clearing markets via bundles"
    - "welfare and revenue guarantees for competitive bundling equilibrium"
    - "combinatorial auctions via posted prices"
    - "understanding incentives mechanism design becomes algorithm design"
    - "optimization with demand oracles"
    - "an impossibility result for truthful combinatorial auctions with submodular valuations"
    - "bayesian incentive compatibility via fractional assignments"
    - "envy free makespan approximation"

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)