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 "on intercept probability minimization under sparse random linear network coding"):
  Abstract: This paper considers a network where a node wishes to transmit a source message to a legitimate receiver in the presence of an eavesdropper. The transmitter secures its transmissions employing a sparse implementation of Random Linear Network Coding (RLNC). A tight approximation to the probability of the eavesdropper recovering the source message is provided. The proposed approximation applies to both the cases where transmissions occur without feedback or where the reliability of the feedback channel is actively impaired -- as it happens when an eavesdropper jams the feedback channel in an attempt to force the source node to keep transmitting after the legitimate receiver has recovered the message. An optimization framework for minimizing the intercept probability by optimizing the sparsity of the RLNC is also presented. Results validate the proposed approximation and quantify the gain provided by our optimization framework over solutions where non-sparse RLNC is used.

Includes members:
  Member "reliability of broadcast communications under sparse random linear network coding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the probability of partial decoding in sparse network coding")
  Member "reliability of multicast under random linear network coding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "operating its g5 dsrc over unlicensed bands a city scale performance evaluation")
  Member "analysis and optimization of sparse random linear network coding for reliable multicast services":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the probability of partial decoding in sparse network coding")
  Member "rethinking the intercept probability of random linear network coding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "secure data offloading strategy for connected and autonomous vehicles")

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)