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: "matching dyadic distributions to channels"
  Abstract: Many communication channels with discrete input have non-uniform capacity achieving probability mass functions (PMF). By parsing a stream of independent and equiprobable bits according to a full prefix-free code, a modu-lator can generate dyadic PMFs at the channel input. In this work, we show that for discrete memoryless channels and for memoryless discrete noiseless channels, searching for good dyadic input PMFs is equivalent to minimizing the Kullback-Leibler distance between a dyadic PMF and a weighted version of the capacity achieving PMF. We define a new algorithm called Geometric Huffman Coding (GHC) and prove that GHC finds the optimal dyadic PMF in O(m \log m) steps where m is the number of input symbols of the considered channel. Furthermore, we prove that by generating dyadic PMFs of blocks of consecutive input symbols, GHC achieves capacity when the block length goes to infinity.

Connected through hyperedge HE-1 (induced by source paper "rate constrained shaping codes for structured sources"):
  with members:
    - "divergence optimal fixed to fixed length distribution matching with shell mapping"
    - "performance of optimal data shaping codes"
    - "constant composition distribution matching"
    - "arithmetic distribution matching"
    - "how to achieve the capacity of asymmetric channels"
    - "fixed to variable length distribution matching"
    - "informational divergence and entropy rate on rooted trees with probabilities"

Connected through hyperedge HE-2 (induced by source paper "on product codes with probabilistic amplitude shaping for high throughput fiber optic systems"):
  with members:
    - "binary message passing decoding of product codes based on generalized minimum distance decoding"
    - "binary message passing decoding of product like codes"
    - "iterative bounded distance decoding of product codes with scaled reliability"
    - "on low complexity decoding of product codes for high throughput fiber optic systems"
    - "energy efficient soft assisted product decoders"
    - "increasing achievable information rates via geometric shaping"
    - "achievable rates for probabilistic shaping"
    - "miscorrection free decoding of staircase codes"

Connected through hyperedge HE-3 (induced by source paper "a compressed sensing approach for distribution matching"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"
    - "generalized approximate message passing decoder for universal sparse superposition codes"
    - "comparison of geometric and probabilistic shaping with application to atsc 3 0"
    - "threshold saturation of spatially coupled sparse superposition codes for all memoryless channels"
    - "proof of threshold saturation for spatially coupled sparse superposition codes"
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"

Connected through hyperedge HE-4 (induced by source paper "prefix free code distribution matching for probabilistic constellation shaping"):
  with members:
    - "shell mapping for distribution matching"
    - "divergence optimal fixed to fixed length distribution matching with shell mapping"
    - "divergence scaling of fixed length binary output one to one distribution matching"
    - "fixed to variable length resolution coding for target distributions"
    - "constellation shaping for bit interleaved ldpc coded apsk"
    - "prefix codes equiprobable words unequal letter costs"

Connected through hyperedge HE-5 (induced by source paper "shell mapping for distribution matching"):
  with members:
    - "constant composition distribution matching"

Connected through hyperedge HE-6 (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"
    - "high throughput probabilistic shaping with product distribution matching"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "divergence scaling of fixed length binary output one to one distribution matching"):
  with members:
    - "optimal quantization for distribution synthesis"
    - "constant composition distribution matching"
    - "arithmetic distribution matching"
    - "how to achieve the capacity of asymmetric channels"
    - "fixed to variable length distribution matching"
    - "effective secrecy reliability confusion and stealth"
    - "operating ldpc codes with zero shaping gap"

Connected through hyperedge HE-8 (induced by source paper "variable to fixed length homophonic coding suitable for asymmetric channel coding"):
  with members:
    - "constant composition distribution matching"
    - "how to achieve the capacity of asymmetric channels"
    - "fixed to variable length distribution matching"
    - "operating ldpc codes with zero shaping gap"

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)