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: "achieving optimal backlog in multi processor cup games"
  Abstract: The single- and multi- processor cup games can be used to model natural problems in areas such as processor scheduling, deamortization, and buffer management. At the beginning of the single-processor cup game, $n$ cups are initially empty. In each step of the game, a filler distributes $1$ unit of water among the cups, and then an emptier selects a cup and removes $1 + \epsilon$ units from that cup. The goal of the emptier is to minimize the amount of water in the fullest cup, also known as the backlog. It is known that the greedy algorithm (i.e., empty the fullest cup) achieves backlog $O(\log n)$, and that no deterministic algorithm can do better. We show that the performance of the greedy algorithm can be greatly improved with a small amount of randomization: After any step $i$, and for any $k \ge \Omega(\log \epsilon^{-1})$, the emptier achieves backlog at most $O(k)$ with probability at least $1 -O(2^{-2^k})$. Whereas bounds for the single-processor cup game have been known for more than fifteen years, proving nontrivial bounds on backlog for the multi-processor extension has remained open. We present a simple analysis of the greedy algorithm for the multi-processor cup game, establishing a backlog of $O(\epsilon^{-1} \log n)$, as long as $\delta$, the game's other speed-augmentation constant, is at least $1/poly(n)$. Turning to randomized algorithms, we encounter an unexpected phenomenon: When the number of processors $p$ is large, the backlog after each step drops to \emph{constant} with large probability. Specifically, we show that if $\delta$ and $\epsilon$ satisfy reasonable constraints, then there exists an algorithm that bounds the backlog after a given step by three or less with probability at least $1 - O(\exp(-\Omega(\epsilon^2 p))$. We further extend the guarantees of our randomized algorithm to consider larger backlogs.

Connected through hyperedge HE-1 (induced by source paper "achieving optimal backlog in the vanilla multi processor cup game"):
  with members:
    - "the minimum backlog problem"
    - "alphabet dependent string searching with wexponential search trees"
    - "streamed graph drawing and the file maintenance problem"
    - "on line indexing for general alphabets via predecessor queries on subsets of an ordered list"

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)