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: "minimizing the maximum end to end network delay hardness algorithm and performance"
  Abstract: We consider the scenario where a source streams a flow at a fixed rate to a receiver across the network, possibly using multiple paths. Each link has a finite capacity. Transmission over a link incurs an integer delay if the rate is within the link capacity, and an unbounded delay otherwise. The objective is to minimize the maximum end-to-end delay experienced by the flow. The problem, denoted as Min-Max-Delay, appears in various practical scenarios, e.g., delay-critical video conferencing using inter-datacenter networks. In this paper, we first show Min-Max-Delay is weakly NP-hard and develop an exact algorithm with pseudo-polynomial time complexity. We then propose a Fully Polynomial Time Approximation Scheme (FPTAS). These results reveal fundamental difference between Min-Max-Delay problem and a similar maximum latency problem studied in the literature, for which is APX-hard and no PTAS exists unless P = NP. Moreover, there exists no exact pseudo-polynomial-time algorithm or constant-approximate algorithm for the maximum latency problem. We evaluate our algorithms in the scenario of routing delay-critical video-conferencing traffic over multiple paths of inter-datacenter networks, using simulations based on Amazon EC2 topology. Our algorithms both achieve the optimal maximum delay performance in all simulation instances, consistently outperforming all state-of-the-art solutions which only obtain sub-optimal maximum delay performance in certain instances. Furthermore, simulation results show that our achieved optimal delay always meet the delay requirement for video conferencing, while sub-optimal delay obtained by the alternatives fail to satisfy video-conferencing delay requirement for up to 15% of simulation instances between certain cross-continental source-receiver pair.

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)