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 simple online fair division problem"):
  Abstract: Random objects arrive sequentially and must be divided among a fixed set of $n$ agents upon arrival: the distribution $\mu$ of the profile of utilities is IID across periods, but arbitrary across agents. We consider a class of online division rules that take, as an input, only the realized utility profile, and from $\mu$ the individual expected utilities. They have no record from past realized utilities, and do not know either if and how many new objects will appear in the future. We call such rules prior-independent.  A rule is fair if each agent, ex ante, expects at least $1/n$-th of his utility for the object if it is a good, at most $1/n$-th of his disutility for it if it is a bad. Among fair prior-independent rules to divide goods (bads) we uncover those collecting the largest (lowest) total expected (dis)utility. There is exactly one fair rule for bads that is optimal in this sense. However, for goods, the set of optimal fair rules is one dimensional. Both in the worst case and in the asymptotic sense, our optimal rules perform much better than the natural Proportional rule (for goods or for bads), and not much worse than the optimal fair prior-dependent rule that knows the full distribution $\mu$ in addition to realized utilities.

Includes members:
  Member "dynamic fair division problem with general valuations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic weighted fairness with minimal disruptions")
  Member "competitive division of a mixed manna":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "equitable allocations of indivisible chores")
  Member "online fair division analysing a food bank problem":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "greedy algorithms for fair division of mixed manna")
  Member "online cake cutting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online fair division a survey")
  Member "online stochastic matching beating 1 1 e":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online graph matching problems with a worst case reassignment budget")

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)