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: "on the power of advice and randomization for online bipartite matching"
  Abstract: While randomized online algorithms have access to a sequence of uniform random bits, deterministic online algorithms with advice have access to a sequence of advice bits, i.e., bits that are set by an all powerful oracle prior to the processing of the request sequence. Advice bits are at least as helpful as random bits, but how helpful are they? In this work, we investigate the power of advice bits and random bits for online maximum bipartite matching (MBM). #R##N#The well-known Karp-Vazirani-Vazirani algorithm is an optimal randomized $(1-\frac{1}{e})$-competitive algorithm for \textsc{MBM} that requires access to $\Theta(n \log n)$ uniform random bits. We show that $\Omega(\log(\frac{1}{\epsilon}) n)$ advice bits are necessary and $O(\frac{1}{\epsilon^5} n)$ sufficient in order to obtain a $(1-\epsilon)$-competitive deterministic advice algorithm. Furthermore, for a large natural class of deterministic advice algorithms, we prove that $\Omega(\log \log \log n)$ advice bits are required in order to improve on the $\frac{1}{2}$-competitiveness of the best deterministic online algorithm, while it is known that $O(\log n)$ bits are sufficient. #R##N#Last, we give a randomized online algorithm that uses $c n$ random bits, for integers $c \ge 1$, and a competitive ratio that approaches $1-\frac{1}{e}$ very quickly as $c$ is increasing. For example if $c = 10$, then the difference between $1-\frac{1}{e}$ and the achieved competitive ratio is less than $0.0002$.

Connected through hyperedge HE-1 (induced by source paper "online bin covering with advice"):
  with members:
    - "a new lower bound for classic online bin packing"
    - "a new and improved algorithm for online bin packing"
    - "a simple ptas for the dual bin packing problem and advice complexity of its online version"
    - "randomization can be as helpful as a glimpse of the future in online computation"
    - "a logarithmic additive integrality gap for bin packing"
    - "online algorithms with advice for bin packing and scheduling problems"
    - "approximating bin packing within o log opt log log opt bins"
    - "online bin packing with advice"

Connected through hyperedge HE-2 (induced by source paper "temporal matching"):
  with members:
    - "stream graphs and link streams for the modeling of interactions over time"
    - "fully polynomial time parameterized computations for graphs and matrices of low treewidth"
    - "computing maximal cliques in link streams"

Connected through hyperedge HE-3 (induced by source paper "an experimental study of algorithms for online bipartite matching"):
  with members:
    - "on conceptually simple algorithms for variants of online bipartite matching"
    - "greedy online bipartite matching on random graphs"
    - "are stable instances easy"
    - "online stochastic matching beating 1 1 e"

Connected through hyperedge HE-4 (induced by source paper "advice complexity of priority algorithms"):
  with members:
    - "on the limitations of deterministic de randomizations for online bipartite matching and max sat"
    - "randomization can be as helpful as a glimpse of the future in online computation"
    - "greedy matching guarantees and limitations"
    - "online bin packing with advice"

Connected through hyperedge HE-5 (induced by source paper "on conceptually simple algorithms for variants of online bipartite matching"):
  with members:
    - "an experimental study of algorithms for online bipartite matching"
    - "on the limitations of deterministic de randomizations for online bipartite matching and max sat"
    - "computing maximum flow with augmenting electrical flows"
    - "new algorithms better bounds and a novel model for online stochastic matching"
    - "deterministic algorithms for submodular maximization problems"
    - "greedy matching guarantees and limitations"
    - "greedy algorithms make efficient mechanisms"
    - "matchings with lower quotas algorithms and complexity"

Connected through hyperedge HE-6 (induced by source paper "a simple ptas for the dual bin packing problem and advice complexity of its online version"):
  with members:
    - "on conceptually simple algorithms for variants of online bipartite matching"
    - "the advice complexity of a class of hard online problems"
    - "online bin packing with advice"

Connected through hyperedge HE-7 (induced by source paper "weighted online problems with advice"):
  with members:
    - "online bounded analysis"
    - "randomization can be as helpful as a glimpse of the future in online computation"
    - "online algorithms with advice for bin packing and scheduling problems"
    - "advice complexity of the online induced subgraph problem"
    - "online makespan minimization with parallel schedules"

Connected through hyperedge HE-8 (induced by source paper "on the limitations of deterministic de randomizations for online bipartite matching and max sat"):
  with members:
    - "randomization can be as helpful as a glimpse of the future in online computation"
    - "deterministic algorithms for submodular maximization problems"
    - "greedy matching guarantees and limitations"
    - "online vertex weighted bipartite matching and single bid budgeted allocations"

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)