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 "variable length convolutional coding for short blocklengths with decision feedback"):
  Abstract: This paper presents a variable-length decision-feedback coding scheme that achieves high rates at short blocklengths. This scheme uses the reliability-output Viterbi algorithm (ROVA) to determine when the receiver's decoding estimate satisfies a given error constraint. We evaluate the performance of both terminated and tail-biting convolutional codes at average blocklengths less than 300 symbols, using the ROVA and the tail-biting ROVA, respectively. Comparing with recent results from finite-blocklength information theory, simulations for both the BSC and the AWGN channel show that the reliability-based decision-feedback scheme can surpass the random-coding lower bound on throughput for feedback codes at some blocklengths less than 100 symbols. This is true both when decoding after every symbol is permitted and when decoding is limited to a small number of increments. Finally, the performance of the reliability-based stopping rule with the ROVA is compared with retransmission decisions based on CRCs. For short blocklengths where the latency overhead of the CRC bits is severe, the ROVA-based approach delivers superior rates.

Includes members:
  Member "convolutional code specific crc code design":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an efficient algorithm for designing optimal crcs for tail biting convolutional codes")
  Member "extrinsic jensen shannon divergence applications to variable length coding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "low complexity algorithms for transmission of short blocks over the bsc with full feedback")
  Member "green communication via type i arq finite block length analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ultra reliable communication via optimum power allocation for harq retransmission schemes")
  Member "finite block length analysis of the incremental redundancy harq":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "physical layer security in the finite blocklength regime over fading channels")
  Member "reliability output decoding of tail biting convolutional codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient error correcting codes in the short blocklength regime")
  Member "quasi static simo fading channels at finite blocklength":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "joint power control and rate allocation enabling ultra reliability and energy efficiency in simo wireless networks")
  Member "a hybrid arq scheme based on polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "design of polar codes in 5g new radio")
  Member "feedback communication systems with limitations on incremental redundancy":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "optimizing transmission lengths for limited feedback with non binary ldpc examples")

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)