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: "sinr outage evaluation in cellular networks saddle point approximation spa using normal inverse gaussian nig distribution"
  Abstract: Signal-to-noise-plus-interference ratio (SINR) outage probability is among one of the key performance metrics of a wireless cellular network. In this paper, we propose a semi-analytical method based on saddle point approximation (SPA) technique to calculate the SINR outage of a wireless system whose SINR can be modeled in the form $\frac{\sum_{i=1}^M X_i}{\sum_{i=1}^N Y_i +1}$ where $X_i$ denotes the useful signal power, $Y_i$ denotes the power of the interference signal, and $\sum_{i=1}^M X_i$, $\sum_{i=1}^N Y_i$ are independent random variables. Both $M$ and $N$ can also be random variables. The proposed approach is based on the saddle point approximation to cumulative distribution function (CDF) as given by \tit{Wood-Booth-Butler formula}. The approach is applicable whenever the cumulant generating function (CGF) of the received signal and interference exists, and it allows us to tackle distributions with large skewness and kurtosis with higher accuracy. In this regard, we exploit a four parameter \tit{normal-inverse Gaussian} (NIG) distribution as a base distribution. Given that the skewness and kurtosis satisfy a specific condition, NIG-based SPA works reliably. When this condition is violated, we recommend SPA based on normal or symmetric NIG distribution, both special cases of NIG distribution, at the expense of reduced accuracy. For the purpose of demonstration, we apply SPA for the SINR outage evaluation of a typical user experiencing a downlink coordinated multi-point transmission (CoMP) from the base stations (BSs) that are modeled by homogeneous Poisson point process. We characterize the outage of the typical user in scenarios such as (a)~when the number and locations of interferers are random, and (b)~when the fading channels and number of interferers are random. Numerical results are presented to illustrate the accuracy of the proposed set of approximations.

Connected through hyperedge HE-1 (induced by source paper "spatial alignment of coding and modulation helps content delivery"):
  with members:
    - "effect of user mobility on the performance of device to device networks with distributed caching"
    - "content delivery in erasure broadcast channels with cache and feedback"
    - "wireless device to device caching networks basic principles and system performance"
    - "wireless caching technical misconceptions and business barriers"
    - "analysis and optimization of caching and multicasting in large scale cache enabled heterogeneous wireless networks"
    - "cooperative caching and transmission design in cluster centric small cell networks"
    - "cache enabled heterogeneous cellular networks optimal tier level content placement"
    - "greendelivery proactive content caching and push with energy harvesting based small cells"

Connected through hyperedge HE-2 (induced by source paper "mitigating interference in content delivery networks by spatial signal alignment the approach of shot noise ratio"):
  with members:
    - "effect of user mobility on the performance of device to device networks with distributed caching"
    - "optimal geographic caching in finite wireless networks"
    - "content delivery in erasure broadcast channels with cache and feedback"
    - "wireless device to device caching networks basic principles and system performance"
    - "wireless caching technical misconceptions and business barriers"
    - "cooperative caching and transmission design in cluster centric small cell networks"
    - "cache enabled heterogeneous cellular networks optimal tier level content placement"
    - "greendelivery proactive content caching and push with energy harvesting based small cells"

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)