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 matching with general arrivals"
  Abstract: The online matching problem was introduced by Karp, Vazirani and Vazirani nearly three decades ago. In that seminal work, they studied this problem in bipartite graphs with vertices arriving only on one side, and presented optimal deterministic and randomized algorithms for this setting. In comparison, more general arrival models, such as edge arrivals and general vertex arrivals, have proven more challenging and positive results are known only for various relaxations of the problem. In particular, even the basic question of whether randomization allows one to beat the trivially-optimal deterministic competitive ratio of $\frac{1}{2}$ for either of these models was open. In this paper, we resolve this question for both these natural arrival models, and show the following. #R##N#1. For edge arrivals, randomization does not help --- no randomized algorithm is better than $\frac{1}{2}$ competitive. #R##N#2. For general vertex arrivals, randomization helps --- there exists a randomized $(\frac{1}{2}+\Omega(1))$-competitive online matching algorithm.

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"
    - "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"
    - "relaxing the irrevocability requirement for online graph algorithms"

Connected through hyperedge HE-2 (induced by source paper "independent sets of dynamic rectangles algorithms and experiments"):
  with members:
    - "fully dynamic maximal independent set in expected poly log update time"
    - "exploring semi automatic map labeling"
    - "fully dynamic maximal independent set with polylogarithmic update time"
    - "fully dynamic maximal independent set with sublinear update time"
    - "a deamortization approach for dynamic spanner and dynamic maximal matching"
    - "dynamic algorithms for graph coloring"
    - "on approximating maximum independent set of rectangles"
    - "interval scheduling and colorful independent sets"

Connected through hyperedge HE-3 (induced by source paper "rounding dynamic matchings against an adaptive adversary"):
  with members:
    - "decremental sssp in weighted digraphs faster and against an adaptive adversary"
    - "deterministic algorithms for decremental approximate shortest paths faster and simpler"
    - "tight bounds for online edge coloring"
    - "distributed weighted matching via randomized composable coresets"
    - "dynamic low stretch trees via dynamic low diameter decompositions"
    - "a new algorithm for decremental single source shortest paths with applications to vertex capacitated flow and cut problems"
    - "towards a unified theory of sparsification for matching problems"
    - "reoptimization via gradual transformations"

Connected through hyperedge HE-4 (induced by source paper "distributed weighted matching via randomized composable coresets"):
  with members:
    - "weighted matchings via unweighted augmentations"
    - "submodular optimization in the mapreduce model"
    - "greedy and local ratio algorithms in the mapreduce model"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "round compression for parallel matching algorithms"
    - "on estimating maximum matching size in graph streams"
    - "almost optimal stochastic weighted matching with few queries"
    - "ignorance is almost bliss near optimal stochastic matching with few queries"

Connected through hyperedge HE-5 (induced by source paper "tight bounds for online edge coloring"):
  with members:
    - "tight competitive ratios of classic matching algorithms in the fully online model"
    - "how to match when all vertices arrive online"
    - "an economic based analysis of ranking for online bipartite matching"
    - "tighter bounds for online bipartite matching"
    - "deterministic distributed edge coloring with fewer colors"
    - "dynamic algorithms for graph coloring"
    - "deterministic distributed edge coloring via hypergraph maximal matching"
    - "online lower bounds via duality"

Connected through hyperedge HE-6 (induced by source paper "online stochastic matching with edge arrivals"):
  with members:
    - "beating greedy for stochastic bipartite matching"
    - "prophet inequality for bipartite matching merits of being simple and non adaptive"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "stochastic online metric matching"):
  with members:
    - "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"
    - "near optimum online ad allocation for targeted advertising"

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)