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: "online vertex weighted bipartite matching and single bid budgeted allocations"
  Abstract: We study the following vertex-weighted online bipartite matching problem: $G(U, V, E)$ is a bipartite graph. The vertices in $U$ have weights and are known ahead of time, while the vertices in $V$ arrive online in an arbitrary order and have to be matched upon arrival. The goal is to maximize the sum of weights of the matched vertices in $U$. When all the weights are equal, this reduces to the classic \emph{online bipartite matching} problem for which Karp, Vazirani and Vazirani gave an optimal $\left(1-\frac{1}{e}\right)$-competitive algorithm in their seminal work~\cite{KVV90}. Our main result is an optimal $\left(1-\frac{1}{e}\right)$-competitive randomized algorithm for general vertex weights. We use \emph{random perturbations} of weights by appropriately chosen multiplicative factors. Our solution constitutes the first known generalization of the algorithm in~\cite{KVV90} in this model and provides new insights into the role of randomization in online allocation problems. It also effectively solves the problem of \emph{online budgeted allocations} \cite{MSVV05} in the case when an agent makes the same bid for any desired item, even if the bid is comparable to his budget - complementing the results of \cite{MSVV05, BJN07} which apply when the bids are much smaller than the budgets.

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 "online primal dual meets online matching with stochastic rewards configuration lp to the rescue"):
  with members:
    - "vertex weighted online stochastic matching with patience constraints"
    - "online matching with stochastic rewards optimal competitive ratio via path based formulation"
    - "tight competitive ratios of classic matching algorithms in the fully online model"
    - "how to match when all vertices arrive online"
    - "maximum weight online matching with deadlines"
    - "online vertex weighted bipartite matching beating 1 1 e with random arrivals"
    - "online stochastic matching online actions based on offline statistics"
    - "online stochastic matching beating 1 1 e"

Connected through hyperedge HE-3 (induced by source paper "a simple 1 1 e approximation for oblivious bipartite matching"):
  with members:
    - "beating greedy for stochastic bipartite matching"
    - "perturbed greedy on oblivious matching problems"
    - "how to match when all vertices arrive online"
    - "the price of information in combinatorial optimization"
    - "the stochastic matching problem beating half with a non adaptive algorithm"

Connected through hyperedge HE-4 (induced by source paper "prophet inequality for bipartite matching merits of being simple and non adaptive"):
  with members:
    - "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"
    - "maximizing efficiency in dynamic matching markets"
    - "makespan minimization via posted prices"
    - "prophet inequalities made easy stochastic optimization by pricing non stochastic inputs"
    - "beyond matroids secretary problem and prophet inequality with general constraints"
    - "online contention resolution schemes"

Connected through hyperedge HE-5 (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"
    - "new algorithms better bounds and a novel model for online stochastic matching"
    - "improved approximation algorithms for stochastic matching"
    - "greedy online bipartite matching on random graphs"

Connected through hyperedge HE-6 (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"
    - "new algorithms better bounds and a novel model for online stochastic matching"

Connected through hyperedge HE-7 (induced by source paper "online multidimensional packing problems in the random order model"):
  with members:
    - "primal beats dual on online packing lps in the random order model"
    - "geometry of online packing linear programs"
    - "online stochastic packing applied to display ad allocation"
    - "a dynamic near optimal algorithm for online linear programming"
    - "combinatorial auctions with decreasing marginal utilities"

Connected through hyperedge HE-8 (induced by source paper "size versus truthfulness in the house allocation problem"):
  with members:
    - "fair assignment of indivisible objects under ordinal preferences"
    - "welfare maximization and truthfulness in mechanism design with ordinal preferences"
    - "social welfare in one sided matching markets without money"
    - "truthful assignment without money"

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)