human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "how to boost the throughput of harq with off the shelf codes"
  Abstract: In this work, we propose a coding strategy designed to enhance the throughput of hybrid ARQ (HARQ) transmissions over i.i.d. block-fading channels with the channel state information (CSI) unknown at the transmitter. We use a joint packet coding where the same channel block is logically shared among many packets. To reduce the complexity, we use a two-layer coding where, first, packets are first coded by the binary compressing encoders, and the results are then passed to the conventional channel encoder. We show how to optimize the compression rates on the basis of the empirical error-rate curves. We also discuss how the parameters of the practical turbo-codes may be modified to take advantage of the proposed HARQ scheme. Finally, simple and pragmatic rate adaptation strategies are developed. In numerical examples, our scheme is compared to the conventional incremental redundancy HARQ (IR-HARQ), and it yields a notable gain of 1-2 dB in the region of high throughput, where HARQ fails to provide any improvement.

Connected through hyperedge HE-1 (induced by source paper "amc and harq how to increase the throughput"):
  with members:
    - "generalized harq protocols with delayed channel state information and average latency constraints"
    - "harq and amc friends or foes"
    - "adaptive cross packet harq"
    - "delayed channel state information incremental redundancy with backtrack retransmission"
    - "performance of hybrid arq in block fading channels a fixed outage probability analysis"

Connected through hyperedge HE-2 (induced by source paper "adaptive cross packet harq"):
  with members:
    - "harq and amc friends or foes"
    - "rate adaptation for secure harq protocols"
    - "harq buffer management an information theoretic view"
    - "delayed channel state information incremental redundancy with backtrack retransmission"
    - "block fading channels with delayed csit at finite blocklength"
    - "on the benefits of partial channel state information for repetition protocols in block fading channels"
    - "mimo arq with multi bit feedback outage analysis"
    - "performance of hybrid arq in block fading channels a fixed outage probability analysis"

Question: Please tell me which class the center node 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)