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 "perpetual codes for network coding"):
  Abstract: Random Linear Network Coding (RLNC) provides a theoretically efficient method for coding. Some of its practical drawbacks are the complexity of decoding and the overhead due to the coding vectors. For computationally weak and battery-driven platforms, these challenges are particular important. In this work, we consider the coding variant Perpetual codes which are sparse, non-uniform and the coding vectors have a compact representation. The sparsity allows for fast encoding and decoding, and the non-uniform protection of symbols enables recoding where the produced symbols are indistinguishable from those encoded at the source. The presented results show that the approach can provide a coding overhead arbitrarily close to that of RLNC, but at reduced computational load. The achieved gain over RLNC grows with the generation size, and both encoding and decoding throughput is approximately one order of magnitude higher compared to RLNC at a generation size of 2048. Additionally, the approach allows for easy adjustment between coding throughput and code overhead, which makes it suitable for a broad range of platforms and applications.

Includes members:
  Member "effects of the generation size and overlap on throughput and complexity in randomized linear network coding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on efficient decoding and design of sparse random linear network codes")
  Member "sparse network coding with overlapping classes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "upper bound scalability on achievable rates of batched codes for line 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.NI(Networking and Internet Architecture)