human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "protograph based ldpc code design for shaped bit metric decoding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "protograph based ldpc code design for probabilistic shaping with on off keying")
  Member "protograph based ldpc code design for bit metric decoding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bandwidth efficient and rate matched low density parity check coded modulation")
  Member "probabilistic signal shaping for bit metric decoding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "achievable information rates for probabilistic amplitude shaping a minimum randomness approach via random sign coding arguments")
  Member "theoretic shaping bounds for single letter constraints and mismatched decoding":
    also connected through other hyperedges:
      (none)
  Member "an efficient algorithm to calculate bicm capacity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "probabilistic signal shaping for bit metric decoding")
  Member "optimal alphabets and binary labelings for bicm at low snr":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multidimensional constellations for uplink scma systems a comparative study")

Question: Please tell me which class the source paper 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)