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: "dynamic matching market design"
  Abstract: We introduce a simple benchmark model of dynamic matching in networked markets, where agents arrive and depart stochastically and the network of acceptable transactions among agents forms a random graph. We analyze our model from three perspectives: waiting, optimization, and information. The main insight of our analysis is that waiting to thicken the market can be substantially more important than increasing the speed of transactions, and this is quite robust to the presence of waiting costs. From an optimization perspective, naive local algorithms, that choose the right time to match agents but do not exploit global network structure, can perform very close to optimal algorithms. From an information perspective, algorithms that employ even partial information on agents' departure times perform substantially better than those that lack such information. To elicit agents' departure times, we design an incentive-compatible continuous-time dynamic mechanism without transfers.

Connected through hyperedge HE-1 (induced by source paper "fair matching in dynamic kidney exchange"):
  with members:
    - "kidney exchange in dynamic sparse heterogenous pools"

Connected through hyperedge HE-2 (induced by source paper "the stochastic matching problem beating half with a non adaptive algorithm"):
  with members:
    - "ignorance is almost bliss near optimal stochastic matching with few queries"
    - "ranking on arbitrary graphs rematch via continuous lp with monotone and boundary condition constraints"
    - "a stochastic probing problem with applications"
    - "maximum matching in semi streaming with few passes"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"

Connected through hyperedge HE-3 (induced by source paper "almost optimal stochastic weighted matching with few queries"):
  with members:
    - "the stochastic matching problem beating half with a non adaptive algorithm"
    - "stochastic packing integer programs with few queries"
    - "ignorance is almost bliss near optimal stochastic matching with few queries"
    - "a stochastic probing problem with applications"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"

Connected through hyperedge HE-4 (induced by source paper "two sided facility location"):
  with members:
    - "approximating gains from trade in two sided markets via simple mechanisms"
    - "stability of service under time of use pricing"
    - "online matching haste makes waste"
    - "minimizing the continuous diameter when augmenting paths and cycles with shortcuts"

Connected through hyperedge HE-5 (induced by source paper "matching while learning"):
  with members:
    - "algorithms with logarithmic or sublinear regret for constrained contextual bandits"
    - "contextual bandits with global constraints and objective"
    - "linear contextual bandits with global constraints and objective"
    - "bandits with concave rewards and convex knapsacks"
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "resourceful contextual bandits"
    - "an information theoretic analysis of thompson sampling"
    - "bandits with knapsacks"

Connected through hyperedge HE-6 (induced by source paper "position indexed formulations for kidney exchange"):
  with members:
    - "ignorance is almost bliss near optimal stochastic matching with few queries"
    - "kidney exchange in dynamic sparse heterogenous pools"

Connected through hyperedge HE-7 (induced by source paper "ignorance is almost bliss near optimal stochastic matching with few queries"):
  with members:
    - "kidney exchange in dynamic sparse heterogenous pools"
    - "a stochastic probing problem with applications"
    - "matching with our eyes closed"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"

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)