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 "instantly decodable versus random linear network coding a comparative framework for throughput and decoding delay performance"):
  Abstract: This paper studies the tension between throughput and decoding delay performance of two widely-used network coding schemes: random linear network coding (RLNC) and instantly decodable network coding (IDNC). A single-hop broadcasting system model is considered that aims to deliver a block of packets to all receivers in the presence of packet erasures. For a fair and analytically tractable comparison between the two coding schemes, the transmission comprises two phases: a systematic transmission phase and a network coded transmission phase which is further divided into rounds. After the systematic transmission phase and given the same packet reception state, three quantitative metrics are proposed and derived in each scheme: 1) the absolute minimum number of transmissions in the first coded transmission round (assuming no erasures), 2) probability distribution of extra coded transmissions in a subsequent round (due to erasures), and 3) average packet decoding delay. This comparative study enables application-aware adaptive selection between IDNC and RLNC after systematic transmission phase. #R##N#One contribution of this paper is to provide a deep and systematic understanding of the IDNC scheme, to propose the notion of packet diversity and an optimal IDNC encoding scheme for minimizing metric 1. This is generally NP-hard, but nevertheless required for characterizing and deriving all the three metrics. Analytical and numerical results show that there is no clear winner between RLNC and IDNC if one is concerned with both throughput and decoding delay performance. IDNC is more preferable than RLNC when the number of receivers is smaller than packet block size, and the case reverses when the number of receivers is much greater than the packet block size. In the middle regime, the choice can depend on the application and a specific instance of the problem.

Includes members:
  Member "dynamic rate adaptation for improved throughput and delay in wireless network coded broadcast":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "high throughput low delay wireless multicast via multi channel moving window codes")
  Member "throughput delay analysis of random linear network coding for wireless broadcasting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "delay complexity trade off of random linear network coding in wireless broadcast")
  Member "effective delay control in online network coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "on the probability of partial decoding in sparse network coding")
  Member "informed network coding for minimum decoding delay":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "packet loss recovery in broadcast for real time applications in dense wireless 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.IT(Information Theory)