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: "serenade a parallel randomized algorithm suite for crossbar scheduling in input queued switches"
  Abstract: Most of today's high-speed switches and routers adopt an input-queued crossbar switch architecture. Such a switch needs to compute a matching (crossbar schedule) between the input ports and output ports during each switching cycle (time slot). A key research challenge in designing large (in number of input/output ports $N$) input-queued crossbar switches is to develop crossbar scheduling algorithms that can compute "high quality" matchings - i.e. those that result in high switch throughput (ideally $100\%$) and low queueing delays for packets - at line rates. SERENA is arguably the best algorithm in that regard: It outputs excellent matching decisions that result in $100\%$ switch throughput and near-optimal queueing delays. However, since SERENA is a centralized algorithm with $O(N)$ computational complexity, it cannot support switches that both are large (in terms of $N$) and have a very high line rate per port. In this work, we propose SERENADE (SERENA, the Distributed Edition), a parallel algorithm suite that emulates SERENA in only $O(\log N)$ %(distributed) iterations between input ports and output ports, and hence has a time complexity of only $O(\log N)$ per port. Through extensive simulations, we show that all three variants in the SERENADE suite can, either provably or empirically, achieve 100\% throughput, and that they have similar delay performances as SERENA under heavy traffic loads.

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.PF(Performance)