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: "peak to average power ratio of good codes for gaussian channel"
  Abstract: Consider a problem of forward error-correction for the additive white Gaussian noise (AWGN) channel. For finite blocklength codes the backoff from the channel capacity is inversely proportional to the square root of the blocklength. In this paper it is shown that codes achieving this tradeoff must necessarily have peak-to-average power ratio (PAPR) proportional to logarithm of the blocklength. This is extended to codes approaching capacity slower, and to PAPR measured at the output of an OFDM modulator. As a by-product the convergence of (Smith's) amplitude-constrained AWGN capacity to Shannon's classical formula is characterized in the regime of large amplitudes. This converse-type result builds upon recent contributions in the study of empirical output distributions of good channel codes.

Connected through hyperedge HE-1 (induced by source paper "on the capacity of the peak power constrained vector gaussian channel an estimation theoretic perspective"):
  with members:
    - "upper and lower bounds on the capacity of amplitude constrained mimo channels"
    - "capacity bounds for discrete time amplitude constrained additive white gaussian noise channels"
    - "on the capacity of vector gaussian channels with bounded inputs"
    - "pointwise relations between information and estimation in gaussian noise"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-2 (induced by source paper "capacity bounds for discrete time amplitude constrained additive white gaussian noise channels"):
  with members:
    - "an upper bound for the capacity of amplitude constrained scalar awgn channel"
    - "dual capacity upper bounds for noisy runlength constrained channels"
    - "on the monotonicity log concavity and tight bounds of the generalized marcum and nuttall q functions"
    - "on the capacity of free space optical intensity channels"

Connected through hyperedge HE-3 (induced by source paper "the third order term in the normal approximation for singular channels"):
  with members:
    - "channels with cost constraints strong converse and dispersion"
    - "second order coding rates for channels with state"
    - "a tight upper bound for the third order asymptotics for most discrete memoryless channels"
    - "mismatched decoding error exponents second order rates and saddlepoint approximations"
    - "moderate deviations in channel coding"
    - "refinement of the sphere packing bound asymmetric channels"
    - "an information theoretic characterization of channels that die"
    - "information spectrum approach to second order coding rate in channel coding"

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)