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: "cache aided combination networks with interference"
  Abstract: Centralized coded caching and delivery is studied for a radio access combination network (RACN), whereby a set of $H$ edge nodes (ENs), connected to a cloud server via orthogonal fronthaul links with limited capacity, serve a total of $K$ user equipments (UEs) over wireless links. Each user, equipped with a cache of size $\mu_R N F$ bits, is connected to a distinct set of $r$ ENs each of which equipped with a cache of size $\mu_T N F$ bits, where $\mu_T$, $\mu_R \in [0,1]$ are the fractional cache capacities of the UEs and the ENs, respectively. The objective is to minimize the normalized delivery time (NDT. Three coded caching and transmission schemes are considered, namely the\textit{ MDS-IA}, \textit{soft-transfer} and \textit{ zero-forcing (ZF)} schemes. The achievable NDT for MDS-IA scheme is presented for $r=2$ and arbitrary fractional cache sizes $\mu_T$ and $\mu_R$, and also for arbitrary value of $r$ and fractional cache size $\mu_T$ when the cache capacity of the UE is above a certain threshold. The achievable NDT for the soft-transfer scheme is presented for arbitrary $r$ and arbitrary fractional cache sizes $\mu_T$ and $\mu_R$. The last scheme utilizes ZF between the ENs and the UEs without the participation of the cloud server in the delivery phase. The achievable NDT for this scheme is presented for an arbitrary value of $r$ when the total cache size at a pair of UE and EN is sufficient to store the whole library, i.e., $\mu_T+\mu_R \geq 1$. The results indicate that the fronthaul capacity determines which scheme achieves a better performance in terms of the NDT, and the soft-transfer scheme becomes favorable as the fronthaul capacity increases.

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)