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: "concatenated coding for the awgn channel with noisy feedback"
  Abstract: The use of open-loop coding can be easily extended to a closed-loop concatenated code if the channel has access to feedback. This can be done by introducing a feedback transmission scheme as an inner code. In this paper, this process is investigated for the case when a linear feedback scheme is implemented as an inner code and, in particular, over an additive white Gaussian noise (AWGN) channel with noisy feedback. To begin, we look to derive an optimal linear feedback scheme by optimizing over the received signal-to-noise ratio. From this optimization, an asymptotically optimal linear feedback scheme is produced and compared to other well-known schemes. Then, the linear feedback scheme is implemented as an inner code to a concatenated code over the AWGN channel with noisy feedback. This code shows improvements not only in error exponent bounds, but also in bit-error-rate and frame-error-rate. It is also shown that the if the concatenated code has total blocklength L and the inner code has blocklength, N, the inner code blocklength should scale as N = O(C/R), where C is the capacity of the channel and R is the rate of the concatenated code. Simulations with low density parity check (LDPC) and turbo codes are provided to display practical applications and their error rate benefits.

Connected through hyperedge HE-1 (induced by source paper "feedback channel communication with low precision arithmetic"):
  with members:
    - "deepcode feedback codes via deep learning"
    - "interactive schemes for the awgn channel with noisy feedback"
    - "variations on a theme by schalkwijk and kailath"
    - "optimal feedback communication via posterior matching"

Connected through hyperedge HE-2 (induced by source paper "communicating over filter and forward relay networks with channel output feedback"):
  with members:
    - "capacity of all nine models of channel output feedback for the two user interference channel"
    - "a joint time invariant filtering approach to the linear gaussian relay problem"
    - "the approximate capacity of the gaussian n relay diamond network"
    - "feedback capacity of stationary gaussian channels"
    - "feedback capacity of the first order moving average gaussian channel"

Connected through hyperedge HE-3 (induced by source paper "concatenated coding using linear schemes for gaussian broadcast channels with noisy channel output feedback"):
  with members:
    - "the awgn bc with mac feedback a reduction to noiseless feedback via interaction"
    - "on the noisy feedback capacity of gaussian broadcast channels"
    - "mimo mac bc duality with linear feedback coding schemes"
    - "an achievable rate region for the broadcast channel with feedback"
    - "capacity of all nine models of channel output feedback for the two user interference channel"
    - "using channel output feedback to increase throughput in hybrid arq"
    - "lqg control approach to gaussian broadcast channels with feedback"
    - "feedback capacity of the gaussian interference channel to within 2 bits"

Connected through hyperedge HE-4 (induced by source paper "interactive schemes for the awgn channel with noisy feedback"):
  with members:
    - "the gaussian channel with noisy feedback improving reliability via interaction"
    - "the gaussian channel with noisy feedback near capacity performance via simple interaction"
    - "gaussian channel with noisy feedback and peak energy constraint"
    - "variations on a theme by schalkwijk and kailath"
    - "error exponents for the gaussian channel with active noisy feedback"
    - "optimal feedback communication via posterior matching"
    - "joint wyner ziv dirty paper coding by modulo lattice modulation"

Connected through hyperedge HE-5 (induced by source paper "the gaussian channel with noisy feedback near capacity performance via simple interaction"):
  with members:
    - "on reliability function of bsc with noisy feedback"
    - "optimal feedback communication via posterior matching"

Connected through hyperedge HE-6 (induced by source paper "upper bound on the capacity of gaussian channels with noisy feedback"):
  with members:
    - "bounds on the achievable rate of noisy feedback gaussian channels under linear feedback coding scheme"
    - "the information flow and capacity of channels with noisy feedback"
    - "feedback capacity of stationary gaussian channels"
    - "the capacity of channels with feedback"

Connected through hyperedge HE-7 (induced by source paper "using channel output feedback to increase throughput in hybrid arq"):
  with members:
    - "capacity of a multiple antenna fading channel with a quantized precoding matrix"

Connected through hyperedge HE-8 (induced by source paper "the information flow and capacity of channels with noisy feedback"):
  with members:
    - "a coding theorem for a class of stationary channels with feedback"
    - "finite state channels with time invariant deterministic feedback"
    - "the capacity of channels with feedback"

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)