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 information rates for probabilistic amplitude shaping a minimum randomness approach via random sign coding arguments"):
  Abstract: Probabilistic amplitude shaping (PAS) is a coded modulation strategy in which constellation shaping and channel coding are combined. PAS has attracted considerable attention in both wireless and optical communications. Achievable information rates (AIRs) of PAS have been investigated in the literature using Gallager's error exponent approach. In particular, it has been shown that PAS achieves the capacity of a memoryless channel. In this work, we revisit the capacity-achieving property of PAS, and derive AIRs using weak typicality. We provide alternative proofs based on random sign-coding arguments. Our objective is to minimize the randomness in the random coding experiment. Accordingly, in our proofs, only some signs of the channel inputs are drawn from a random code, while the remaining signs and the amplitudes are produced constructively. We consider both symbol-metric and bit-metric decoding.

Includes members:
  Member "information rates and error exponents for probabilistic amplitude shaping":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "one and two bit message passing for sc ldpc codes with higher order modulation")
  Member "achievable rates for probabilistic shaping":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on product codes with probabilistic amplitude shaping for high throughput fiber optic systems")
  Member "achievable rates for shaped bit metric decoding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sign bit shaping using polar codes")
  Member "probabilistic signal shaping for bit metric decoding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "awgn goodness is enough capacity achieving lattice codes based on dithered probabilistic shaping")

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)