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 "high throughput probabilistic shaping with product distribution matching"):
  Abstract: Product distribution matching (PDM) is proposed to generate target distributions over large alphabets by combining the output of several parallel distribution matchers (DMs) with smaller output alphabets. The parallel architecture of PDM enables low-complexity and high-throughput implementation. PDM is used as a shaping device for probabilistic amplitude shaping (PAS). For 64-ASK and a spectral efficiency of 4.5 bits per channel use (bpcu), PDM is as power efficient as a single full-fledged DM. It is shown how PDM enables PAS for parallel channels present in multi-carrier systems like digital subscriber line (DSL) and orthogonal frequency-division multiplexing (OFDM). The key feature is that PDM shares the DMs for lower bit-levels among different sub-carriers, which improves the power efficiency significantly. A representative parallel channel example shows that PAS with PDM is 0.93 dB more power efficient than conventional uniform signaling and PDM is 0.35 dB more power efficient than individual per channel DMs.

Includes members:
  Member "divergence scaling of fixed length binary output one to one distribution matching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "shaped on off keying using polar codes")
  Member "probabilistic shaping and non binary codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ultra sparse non binary ldpc codes for probabilistic amplitude shaping")
  Member "matching dyadic distributions to channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rate constrained shaping codes for structured sources")

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)