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 "stochastic ordering of interferences in large scale wireless networks"):
  Abstract: Stochastic orders are binary relations defined on probability distributions which capture intuitive notions like being larger or being more variable. This paper introduces stochastic ordering of interference distributions in large-scale networks modeled as point process. Interference is the main performance-limiting factor in most wireless networks, thus it is important to understand its statistics. Since closed-form results for the distribution of interference for such networks are only available in limited cases, interference of networks are compared using stochastic orders, even when closed form expressions for interferences are not tractable. We show that the interference from a large-scale network depends on the fading distributions with respect to the stochastic Laplace transform order. The condition for path-loss models is also established to have stochastic ordering between interferences. The stochastic ordering of interferences between different networks are also shown. Monte-Carlo simulations are used to supplement our analytical results.

Includes members:
  Member "applications of stochastic ordering to wireless communications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transmit power policy and ergodic multicast rate analysis of cognitive radio networks in generalized fading")
  Member "information theoretic capacity of clustered random networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "laplace functional ordering of point processes in large scale wireless networks")
  Member "performance of interference alignment in clustered wireless ad hoc networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalized interference alignment part ii application to wireless secrecy")
  Member "a new outer bound and the noisy interference sum rate capacity for gaussian interference channels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "vector gaussian successive refinement with degraded side information")
  Member "interference and outage in clustered wireless ad hoc networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "smart radio environments empowered by ai reconfigurable meta surfaces an idea whose time has come")

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)