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: "tight analysis of priority queuing policy for egress traffic"
  Abstract: Recently, the problems of evaluating performances of switches and routers have been formulated as online problems, and a great amount of results have been presented. In this paper, we focus on managing outgoing packets (called {\em egress traffic}) on switches that support Quality of Service (QoS), and analyze the performance of one of the most fundamental scheduling policies {\em Priority Queuing} ($PQ$) using competitive analysis. We formulate the problem of managing egress queues as follows: An output interface is equipped with $m$ queues, each of which has a buffer of size $B$. The size of a packet is unit, and each buffer can store up to $B$ packets simultaneously. Each packet is associated with one of $m$ priority values $\alpha_{j}$ ($1 \leq j \leq m$), where $\alpha_{1} \leq \alpha_{2} \leq \cdots \leq \alpha_{m}$, $\alpha_{1} = 1$, and $\alpha_{m} = \alpha$ and the task of an online algorithm is to select one of $m$ queues at each scheduling step. The purpose of this problem is to maximize the sum of the values of the scheduled packets. For any $B$ and any $m$, we show that the competitive ratio of $PQ$ is exactly $2 - \min_{x \in [1, m-1] } \{ \frac{ \alpha_{x+1} }{ \sum_{j = 1}^{x+1} \alpha_{j} } \}$. That is, we conduct a complete analysis of the performance of $PQ$ using worst case analysis. Moreover, we show that no deterministic online algorithm can have a competitive ratio smaller than $1 + \frac{ \alpha^3 + \alpha^2 + \alpha }{ \alpha^4 + 4 \alpha^3 + 3 \alpha^2 + 4 \alpha + 1 }$.

Connected through hyperedge HE-1 (induced by source paper "new competitiveness bounds for the shared memory switch"):
  with members:
    - "fifo queueing policies for packets with heterogeneous processing"

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)