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 the probability of partial decoding in sparse network coding"):
  Abstract: Sparse Network Coding (SNC) has been a promising network coding scheme as an improvement for Random Linear Network Coding (RLNC) in terms of the computational complexity. However, in this literature, there has been no analytical expressions for the probability of decoding a fraction of source messages after transmission of some coded packets. In this work, we looked into the problem of the probability of decoding a fraction of source messages, i.e., partial decoding, in the decoder for a system which uses SNC. We exploited the Principle of Inclusion and Exclusion to derive expressions of partial decoding probability. The presented model predicts the probability of partial decoding with an average deviation of 6%. Our results show that SNC has a great potential for recovering a fraction of the source message, especially in higher sparsity and lower Galois Field size. Moreover, to achieve a better probability of partial decoding throughout transmissions, we define a sparsity tuning scheme that significantly increases the probability of partial decoding. Our results show that this tuning scheme achieves a 16% improvement in terms of probability of decoding a fraction of source packets with respect to traditional SNC.

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 "random linear network coding for 5g mobile video delivery")
  Member "analysis and optimization of sparse random linear network coding for reliable multicast services":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "agile data offloading over novel fog computing infrastructure for cavs")
  Member "probability of partially decoding network coded messages":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secure multi source multicast")
  Member "binary systematic network coding for progressive packet decoding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reliability of multicast under random linear network coding")
  Member "feedback based online network coding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "high throughput low delay wireless multicast via multi channel moving window codes")
  Member "intermediate performance of rateless codes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "low density majority codes and the problem of graceful degradation")

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)