human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "mitigating interference in content delivery networks by spatial signal alignment the approach of shot noise ratio"):
  Abstract: Multimedia content especially videos is expected to dominate data traffic in next-generation mobile networks. Caching popular content at the network edge has emerged to be a solution for low-latency content delivery. Compared with the traditional wireless communication, content delivery has a key characteristic that many signals coexisting in the air carry identical popular content. They, however, can interfere with each other at a receiver if their modulation-and-coding (MAC) schemes are adapted to individual channels following the classic approach. To address this issue, we present a novel idea of content adaptive MAC (CAMAC) where adapting MAC schemes to content ensures that all signals carry identical content are encoded using an identical MAC scheme, achieving spatial MAC alignment. Consequently, interference can be harnessed as signals, to improve the reliability of wireless delivery. In the remaining part of the paper, we focus on quantifying the gain CAMAC can bring to a content-delivery network using a stochastic-geometry model. Specifically, content helpers are distributed as a Poisson point process, each of which transmits a file from a content database based on a given popularity distribution. It is discovered that the successful content-delivery probability is closely related to the distribution of the ratio of two independent shot noise processes, named a shot-noise ratio. The distribution itself is an open mathematical problem that we tackle in this work. Using stable-distribution theory and tools from stochastic geometry, the distribution function is derived in closed form. Extending the result in the context of content-delivery networks with CAMAC yields the content-delivery probability in different closed forms. In addition, the gain in the probability due to CAMAC is shown to grow with the level of skewness in the content popularity distribution.

Includes members:
  Member "sinr outage evaluation in cellular networks saddle point approximation spa using normal inverse gaussian nig distribution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spatial alignment of coding and modulation helps content delivery")
  Member "effect of user mobility on the performance of device to device networks with distributed caching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mobility aware content preference learning in decentralized caching networks")
  Member "optimal geographic caching in finite wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stochastic geometry modeling and analysis of single and multi cluster wireless networks")
  Member "content delivery in erasure broadcast channels with cache and feedback":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fundamental limits of wireless caching under mixed cacheable and uncacheable traffic")
  Member "wireless device to device caching networks basic principles and system performance":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "joint optimization of file placement and delivery in cache assisted wireless networks with limited lifetime and cache space")
  Member "wireless caching technical misconceptions and business barriers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fundamental limits of wireless caching under mixed cacheable and uncacheable traffic")
  Member "cooperative caching and transmission design in cluster centric small cell networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reinforcement learning based cooperative coded caching under dynamic popularities in ultra dense networks")
  Member "cache enabled heterogeneous cellular networks optimal tier level content placement":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "reinforcement learning based cooperative coded caching under dynamic popularities in ultra dense networks")

Question: Please tell me which class the source paper 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)