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: "prophet inequality for bipartite matching merits of being simple and non adaptive"
  Abstract: We consider Bayesian online selection problem of a matching in bipartite graphs, i.e., online weighted matching problem with edge arrivals where online algorithm knows distributions of weights, that corresponds to the intersection of two matroids in [Kleinberg and Wienberg STOC 12] model. We consider a simple class of non adaptive vertex-additive policies that assign static prices to all vertices in the graph and accept each edge only if its weight exceeds the sum of the prices of the edge's endpoints. We show existence of a vertex-additive policy with the expected payoff of at least one third of the prophet's payoff and present gradient decent type algorithm that quickly converges to the desired vector of vertex prices. This improves the adaptive online policy of [Kleinberg and Wienberg STOC 12] for the intersection of two matroids in two ways: our policy is non adaptive and has better approximation guarantee of $3$ instead of previous guarantee of $5.82$ against the prophet. We give a complementary lower bound of $2.25$ for any online algorithm in the bipartite matching setting.

Connected through hyperedge HE-1 (induced by source paper "online stochastic max weight matching prophet inequality for vertex and edge arrival models"):
  with members:
    - "beating greedy for stochastic bipartite matching"
    - "online stochastic matching with edge arrivals"
    - "optimal single choice prophet inequalities from samples"
    - "an optimal monotone contention resolution scheme for bipartite matchings via a polyhedral viewpoint"
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "tight competitive ratios of classic matching algorithms in the fully online model"
    - "how to match when all vertices arrive online"
    - "prophets and secretaries with overbooking"

Connected through hyperedge HE-2 (induced by source paper "optimal single choice prophet inequalities from samples"):
  with members:
    - "prophet secretary through blind strategies"
    - "nearly optimal pricing algorithms for production constrained and laminar bayesian selection"
    - "random order contention resolution schemes"
    - "prophet secretary surpassing the 1 1 e barrier"
    - "pricing for online resource allocation intervals and paths"
    - "prophet secretary for combinatorial auctions and matroids"
    - "combinatorial prophet inequalities"
    - "beyond matroids secretary problem and prophet inequality with general constraints"

Connected through hyperedge HE-3 (induced by source paper "online stochastic matching with edge arrivals"):
  with members:
    - "beating greedy for stochastic bipartite matching"
    - "online matching with general arrivals"
    - "tight competitive ratios of classic matching algorithms in the fully online model"
    - "how to match when all vertices arrive online"
    - "online matroid intersection beating half for random arrival"
    - "improved approximation algorithms for stochastic matching"
    - "improved bounds for online preemptive matching"
    - "buyback problem approximate matroid intersection with cancellation costs"

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)