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 "adaptive causal network coding with feedback"):
  Abstract: We propose a novel adaptive and causal random linear network coding (AC-RLNC) algorithm with forward error correction (FEC) for a point-to-point communication channel with delayed feedback. AC-RLNC is adaptive to the channel condition, that the algorithm estimates, and is causal, as coding depends on the particular erasure realizations, as reflected in the feedback acknowledgments. Specifically, the proposed model can learn the erasure pattern of the channel via feedback acknowledgments, and adaptively adjust its retransmission rates using a priori and posteriori algorithms. By those adjustments, AC-RLNC achieves the desired delay and throughput, and enables transmission with zero error probability. We upper bound the throughput and the mean and maximum in order delivery delay of AC-RLNC, and prove that for the point to point communication channel in the non-asymptotic regime the proposed code may achieve more than 90% of the channel capacity. To upper bound the throughput we utilize the minimum Bhattacharyya distance for the AC-RLNC code. We validate those results via simulations. We contrast the performance of AC-RLNC with the one of selective repeat (SR)-ARQ, which is causal but not adaptive, and is a posteriori. Via a study on experimentally obtained commercial traces, we demonstrate that a protocol based on AC-RLNC can, vis-`a-vis SR-ARQ, double the throughput gains, and triple the gain in terms of mean in order delivery delay when the channel is bursty. Furthermore, the difference between the maximum and mean in order delivery delay is much smaller than that of SR-ARQ. Closing the delay gap along with boosting the throughput is very promising for enabling ultra-reliable low-latency communications (URLLC) applications.

Includes members:
  Member "resource allocation and harq optimization for urllc traffic in 5g wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust urllc packet scheduling of ofdm systems")
  Member "tiny codes for guaranteeable delay":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "low latency network adaptive error control for interactive streaming")
  Member "complete mdp convolutional codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a simplified criterion for mdp convolutional codes")
  Member "throughput maximization for delay sensitive random access communication":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on error rate analysis for urllc over multiple fading channels")
  Member "analysis of feedback error in automatic repeat request":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tiny codes for guaranteeable delay")
  Member "low delay random linear coding over a stream":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adaptive causal network coding with feedback for multipath multi hop communications")
  Member "the effect of block wise feedback on the throughput delay trade off in streaming":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "low latency network adaptive error control for interactive streaming")
  Member "an elias bound on the bhattacharyya distance of codes for channels with a zero error capacity":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "variable length coding for zero error channel capacity")

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)