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: "achievable rates for shaped bit metric decoding"
  Abstract: A new achievable rate for bit-metric decoding (BMD) is derived using random coding arguments. The rate expression can be evaluated for any input distribution, and in particular the bit-levels of binary input labels can be stochastically dependent. Probabilistic shaping with dependent bit-levels (shaped BMD), shaping of independent bit-levels (bit-shaped BMD) and uniformly distributed independent bit-levels (uniform BMD) are evaluated on the additive white Gaussian noise (AWGN) channel with Gray labeled bipolar amplitude shift keying (ASK). For 32-ASK at a rate of 3.8 bits/channel use, the gap to 32-ASK capacity is 0.008 dB for shaped BMD, 0.46 dB for bit-shaped BMD, and 1.42 dB for uniform BMD. These numerical results illustrate that dependence between the bit-levels is beneficial on the AWGN channel. The relation to the LM rate and the generalized mutual information (GMI) is discussed.

Connected through hyperedge HE-1 (induced by source paper "achievable information rates for probabilistic amplitude shaping a minimum randomness approach via random sign coding arguments"):
  with members:
    - "information rates and error exponents for probabilistic amplitude shaping"
    - "achievable rates for probabilistic shaping"
    - "probabilistic signal shaping for bit metric decoding"

Connected through hyperedge HE-2 (induced by source paper "sign bit shaping using polar codes"):
  with members:
    - "probabilistically shaped multi level coding with polar codes for fading channels"
    - "code constructions for physical unclonable functions and biometric secrecy systems"
    - "efficient polar code construction for higher order modulation"
    - "polar codes for broadcast channels"
    - "llr based successive cancellation list decoding of polar codes"
    - "how to achieve the capacity of asymmetric channels"
    - "list decoding of polar codes"
    - "a pragmatic coded modulation scheme for high spectral efficiency fiber optic communications"

Connected through hyperedge HE-3 (induced by source paper "trainable communication systems concepts and prototype"):
  with members:
    - "deep learning for the degraded broadcast channel"
    - "joint learning of geometric and probabilistic constellation shaping"
    - "end to end deep learning of optical fiber communications"
    - "online label recovery for deep learning based communication through error correcting codes"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "model free training of end to end communication systems"
    - "an introduction to deep learning for the physical layer"
    - "learning to decode linear codes using deep learning"

Connected through hyperedge HE-4 (induced by source paper "polar codes with integrated probabilistic shaping for 5g new radio"):
  with members:
    - "how to achieve the capacity of asymmetric channels"
    - "list decoding of polar codes"

Connected through hyperedge HE-5 (induced by source paper "on probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel"):
  with members:
    - "sensitivity gains by mismatched probabilistic shaping for optical communication systems"
    - "impact of 4d channel distribution on the achievable rates in coherent optical communication experiments"
    - "constellation shaping for wdm systems using 256qam 1024qam with probabilistic optimization"
    - "experimental demonstration of capacity increase and rate adaptation by probabilistically shaped 64 qam"
    - "on achievable rates for long haul fiber optic communications"
    - "constant composition distribution matching"
    - "ldpc coded modulation with probabilistic shaping for optical fiber systems"
    - "four dimensional coded modulation with bit wise decoders for future optical communications"

Connected through hyperedge HE-6 (induced by source paper "sensitivity gains by mismatched probabilistic shaping for optical communication systems"):
  with members:
    - "experimental demonstration of capacity increase and rate adaptation by probabilistically shaped 64 qam"
    - "on achievable rates for long haul fiber optic communications"
    - "constant composition distribution matching"
    - "ldpc coded modulation with probabilistic shaping for optical fiber systems"
    - "a pragmatic coded modulation scheme for high spectral efficiency fiber optic communications"

Connected through hyperedge HE-7 (induced by source paper "design of robust protograph based ldpc codes for rate adaptation via probabilistic shaping"):
  with members:
    - "experimental demonstration of capacity increase and rate adaptation by probabilistically shaped 64 qam"
    - "protograph based ldpc code design for shaped bit metric decoding"
    - "constant composition distribution matching"
    - "deterministic constructions for large girth protograph ldpc codes"

Connected through hyperedge HE-8 (induced by source paper "protograph based ldpc code design for shaped bit metric decoding"):
  with members:
    - "triggering wave like convergence of tail biting spatially coupled ldpc codes single and dual channel setup"
    - "protograph based ldpc code design for bit metric decoding"
    - "constant composition distribution matching"
    - "optimized bit mappings for spatially coupled ldpc codes over parallel binary erasure channels"
    - "probabilistic signal shaping for bit metric decoding"
    - "deterministic constructions for large girth protograph ldpc codes"

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)