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: "performance evaluation of marsala with synchronisation errors in satellite communications"
  Abstract: Multi-replicA decoding using correlation baSed LocAlisAtion (MARSALA) is a recent random access method for satellite communications. It follows the multiple transmission and interference cancellation scheme of Contention Resolution Diversity Slotted Aloha (CRDSA). Besides, at the receiver side, MARSALA uses autocorrelation to localise replicas of a same packet so as to combine them. This signal combination increases the decoding probability of the packet, especially in case of collisions. Previous work has shown good performance of MARSALA with an assumption of perfect signal combination. However, phase and timing parameters are unknown at the receiver and they are different for the replicas on separate time slots . In this paper, we describe a method to estimate and compensate the timing and phase differences between the replicas prior to signal combination. We also propose to enhance the signal combination by using the principle of Maximum Ratio Combining (MRC). Then, we evaluate the impact of synchronisation errors on MARSALA using our estimation method. We also assess the performance gain achieved with MRC. Finally we compare the performance of MARSALA and CRDSA in various scenarios.

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.IT(Information Theory)