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: "new algorithms better bounds and a novel model for online stochastic matching"
  Abstract: Online matching has received significant attention over the last 15 years due to its close connection to Internet advertising. As the seminal work of Karp, Vazirani, and Vazirani has an optimal (1 - 1/e) competitive ratio in the standard adversarial online model, much effort has gone into developing useful online models that incorporate some stochasticity in the arrival process. One such popular model is the known I.I.D. model where different customer types arrive online from a known distribution. We develop algorithms with improved competitive ratios for some basic variants of this model with integral arrival rates, including (a) the case of general weighted edges, where we improve the best-known ratio of 0.667 due to Haeupler, Mirrokni and Zadimoghaddam to 0.705 and (b) the vertex-weighted case, where we improve the 0.7250 ratio of Jaillet and Lu to 0.7299. We also consider two extensions, one is known I.I.D. with non-integral arrival rate and stochastic rewards. The other is known I.I.D. b-matching with non-integral arrival rate and stochastic rewards. We present a simple non-adaptive algorithm which works well simultaneously on the two extensions.

Connected through hyperedge HE-1 (induced by source paper "online graph matching problems with a worst case reassignment budget"):
  with members:
    - "beating greedy for stochastic bipartite matching"
    - "online matching with general arrivals"
    - "stochastic online metric matching"
    - "understanding zadimoghaddam s edge weighted online matching algorithm unweighted case"
    - "tight competitive ratios of classic matching algorithms in the fully online model"
    - "online maximum matching with recourse"
    - "how to match when all vertices arrive online"
    - "online vertex weighted bipartite matching beating 1 1 e with random arrivals"

Connected through hyperedge HE-2 (induced by source paper "beating greedy for stochastic bipartite matching"):
  with members:
    - "the price of information in combinatorial optimization"
    - "the stochastic matching problem beating half with a non adaptive algorithm"
    - "prophet secretary for combinatorial auctions and matroids"
    - "almost optimal stochastic weighted matching with few queries"
    - "stochastic packing integer programs with few queries"
    - "ignorance is almost bliss near optimal stochastic matching with few queries"
    - "improved approximation algorithms for stochastic matching"
    - "the query commit problem"

Connected through hyperedge HE-3 (induced by source paper "the bayesian prophet a low regret framework for online decision making"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "online stochastic matching new algorithms and bounds"
    - "prophet inequalities made easy stochastic optimization by pricing non stochastic inputs"
    - "online convex optimization using predictions"
    - "algorithms with logarithmic or sublinear regret for constrained contextual bandits"
    - "fast algorithms for online stochastic convex programming"
    - "bandits with knapsacks"
    - "primal beats dual on online packing lps in the random order model"

Connected through hyperedge HE-4 (induced by source paper "vertex weighted online stochastic matching with patience constraints"):
  with members:
    - "online matching with stochastic rewards optimal competitive ratio via path based formulation"
    - "greedy bipartite matching in random type poisson arrival model"
    - "attenuate locally win globally an attenuation based framework for online stochastic matching with timeouts"
    - "an experimental study of algorithms for online bipartite matching"
    - "online vertex weighted bipartite matching beating 1 1 e with random arrivals"
    - "improved approximation algorithms for stochastic matching"
    - "greedy online bipartite matching on random graphs"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"

Connected through hyperedge HE-5 (induced by source paper "stochastic online metric matching"):
  with members:
    - "online matching with general arrivals"
    - "tight competitive ratios of classic matching algorithms in the fully online model"
    - "optimal analysis of an online algorithm for the bipartite matching problem on a line"
    - "how to match when all vertices arrive online"
    - "online vertex weighted bipartite matching beating 1 1 e with random arrivals"
    - "stochastic k server how should uber work"
    - "online allocation with traffic spikes mixing adversarial and stochastic models"
    - "near optimum online ad allocation for targeted advertising"

Connected through hyperedge HE-6 (induced by source paper "permutation strikes back the power of recourse in online metric matching"):
  with members:
    - "stochastic online metric matching"
    - "optimal analysis of an online algorithm for the bipartite matching problem on a line"
    - "maintaining perfect matchings at low cost"
    - "fully dynamic bin packing with limited repacking"
    - "a match in time saves nine deterministic online matching with delays"
    - "a collection of lower bounds for online matching on the line"
    - "online and dynamic algorithms for set cover"
    - "online matching haste makes waste"

Connected through hyperedge HE-7 (induced by source paper "online matching with stochastic rewards optimal competitive ratio via path based formulation"):
  with members:
    - "online allocation of reusable resources achieving optimal competitive ratio"
    - "online primal dual meets online matching with stochastic rewards configuration lp to the rescue"
    - "vertex weighted online stochastic matching with patience constraints"
    - "budget optimal task allocation for reliable crowdsourcing systems"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"
    - "online stochastic matching online actions based on offline statistics"
    - "online stochastic matching beating 1 1 e"
    - "algorithms for secretary problems on graphs and hypergraphs"

Connected through hyperedge HE-8 (induced by source paper "attenuate locally win globally an attenuation based framework for online stochastic matching with timeouts"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "improved approximation algorithms for stochastic matching"
    - "fast algorithms for online stochastic convex programming"
    - "a stochastic probing problem with applications"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"
    - "online stochastic matching online actions based on offline statistics"
    - "online stochastic packing applied to display ad allocation"
    - "a dynamic near optimal algorithm for online linear programming"

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.DS(Data Structures and Algorithms)