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: "a rate compatible sphere packing analysis of feedback coding with limited retransmissions"
  Abstract: Recent work by Polyanskiy et al. and Chen et al. has excited new interest in using feedback to approach capacity with low latency. Polyanskiy showed that feedback identifying the first symbol at which decoding is successful allows capacity to be approached with surprisingly low latency. This paper uses Chen's rate-compatible sphere-packing (RCSP) analysis to study what happens when symbols must be transmitted in packets, as with a traditional hybrid ARQ system, and limited to relatively few (six or fewer) incremental transmissions. Numerical optimizations find the series of progressively growing cumulative block lengths that enable RCSP to approach capacity with the minimum possible latency. RCSP analysis shows that five incremental transmissions are sufficient to achieve 92% of capacity with an average block length of fewer than 101 symbols on the AWGN channel with SNR of 2.0 dB. The RCSP analysis provides a decoding error trajectory that specifies the decoding error rate for each cumulative block length. Though RCSP is an idealization, an example tail-biting convolutional code matches the RCSP decoding error trajectory and achieves 91% of capacity with an average block length of 102 symbols on the AWGN channel with SNR of 2.0 dB. We also show how RCSP analysis can be used in cases where packets have deadlines associated with them (leading to an outage probability).

Connected through hyperedge HE-1 (induced by source paper "energy latency tradeoff in ultra reliable low latency communication with retransmissions"):
  with members:
    - "generalized harq protocols with delayed channel state information and average latency constraints"
    - "finite block length analysis of the incremental redundancy harq"
    - "information spectrum approach to second order coding rate in channel coding"
    - "coding versus arq in fading channels how reliable should the phy be"

Connected through hyperedge HE-2 (induced by source paper "optimizing transmission lengths for limited feedback with non binary ldpc examples"):
  with members:
    - "isn t hybrid arq sufficient"
    - "protograph based raptor like ldpc codes"
    - "reliability output decoding of tail biting convolutional codes"
    - "a hybrid arq scheme based on polar codes"
    - "feedback communication systems with limitations on incremental redundancy"
    - "polar coded harq scheme with chase combining"

Connected through hyperedge HE-3 (induced by source paper "optimizing pilot length for a go no go decision in two state block fading channels with feedback"):
  with members:
    - "chernoff bounds for analysis of rate compatible sphere packing with numerous transmissions"
    - "optimization of training and feedback overhead for beamforming over block fading channels"
    - "training and feedback optimization for multiuser mimo downlink"

Connected through hyperedge HE-4 (induced by source paper "feedback communication systems with limitations on incremental redundancy"):
  with members:
    - "active sequential hypothesis testing"
    - "variations on a theme by schalkwijk and kailath"
    - "error exponents for variable length block codes with feedback and cost constraints"

Connected through hyperedge HE-5 (induced by source paper "reliability based error detection for feedback communication with low latency"):
  with members:
    - "variable length coding with feedback finite length codewords and periodic decoding"

Connected through hyperedge HE-6 (induced by source paper "chernoff bounds for analysis of rate compatible sphere packing with numerous transmissions"):
  with members:
    - "variations on a theme by schalkwijk and kailath"

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)