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 "comments and reply comments on cooperative density estimation in random wireless ad hoc networks"):
  Abstract: In Onur  et al.  [“Cooperative density estimation in random wireless ad hoc networks,”  IEEE Commun. Lett. , vol. 16, no. 3, 269 pp. 331–333, Mar. 2012], two novel density estimation (DE) approaches in wireless random networks were introduced by Onur  et al. , which are carried out respectively in cooperative and individual fashions. Both of them were derived via the maximum likelihood (ML) method. However, an implicit but fatal error was made obtaining the individual DE (I-DE) approach. This letter comments on Onur  et al.  and points out the aforementioned error. By investigating the distance order statistics (DOS) distributions in the random networks, the correct I-DE approach is presented and discussed. Simulation results also show that the correct I-DE outperforms the wrong one. More importantly, a new method that can obtain any univariate or multivariate DOS distribution is demonstrated, which is expected to be helpful for the study of the wireless communications and networking.

Includes members:
  Member "distance distributions in finite uniformly random networks theory and applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "air to air communications beyond 5g a novel 3d comp transmission scheme")
  Member "a geometric interpretation of fading in wireless networks theory and applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secrecy analysis of random mimo wireless networks over alpha mu fading channels")

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)