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: "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.

Connected through hyperedge HE-1 (induced by source paper "probabilistic amplitude shaping with hard decision decoding and staircase codes"):
  with members:
    - "probabilistically shaped coded modulation with hard decision decoding for coherent optical systems"
    - "achievable rates for probabilistic shaping"
    - "on probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel"
    - "density evolution for deterministic generalized product codes with higher order modulation"
    - "experimental demonstration of capacity increase and rate adaptation by probabilistically shaped 64 qam"
    - "density evolution for deterministic generalized product codes on the binary erasure channel at high rates"
    - "constant composition distribution matching"
    - "arithmetic distribution matching"

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)