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: "constant composition distribution matching"
  Abstract: Distribution matching transforms independent and Bernoulli(1/2) distributed input bits into a sequence of output symbols with a desired distribution. Fixed-to-fixed length, invertible, and low complexity encoders and decoders based on constant composition and arithmetic coding are presented. Asymptotically in the blocklength, the encoder achieves the maximum rate, namely the entropy of the desired distribution. Furthermore, the normalized divergence of the encoder output and the desired distribution goes to zero in the blocklength.

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"
    - "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"
    - "matching dyadic distributions to channels"

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 "one and two bit message passing for sc ldpc codes with higher order modulation"):
  with members:
    - "a novel soft aided bit marking decoder for product codes"
    - "binary message passing decoding of product like codes"
    - "protograph based ldpc code design for ternary message passing decoding"
    - "energy efficient soft assisted product decoders"
    - "information rates and error exponents for probabilistic amplitude shaping"
    - "windowed decoding of protograph based ldpc convolutional codes over erasure channels"
    - "staircase codes fec for 100 gb s otn"
    - "analysis and design of binary message passing decoders"

Connected through hyperedge HE-4 (induced by source paper "joint learning of geometric and probabilistic constellation shaping"):
  with members:
    - "deep learning for channel coding via neural mutual information estimation"
    - "geometric constellation shaping for fiber optic communication systems via end to end learning"
    - "model free training of end to end communication systems"
    - "an introduction to deep learning for the physical layer"
    - "on probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel"
    - "adam a method for stochastic optimization"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"
    - "on the partition function and random maximum a posteriori perturbations"

Connected through hyperedge HE-5 (induced by source paper "shaped on off keying using polar codes"):
  with members:
    - "probabilistic parity shaping for linear codes"
    - "protograph based ldpc code design for probabilistic shaping with on off keying"
    - "polar coded pulse position modulation for the poisson channel"
    - "divergence scaling of fixed length binary output one to one distribution matching"
    - "code design for short blocks a survey"
    - "how to achieve the capacity of asymmetric channels"
    - "list decoding of polar codes"
    - "how to construct polar codes"

Connected through hyperedge HE-6 (induced by source paper "finite precision implementation of arithmetic coding based distribution matchers"):
  with members:
    - "arithmetic coding based multi composition codes for bit level distribution matching"
    - "polar codes with integrated probabilistic shaping for 5g new radio"
    - "shell mapping for distribution matching"
    - "experimental demonstration of capacity increase and rate adaptation by probabilistically shaped 64 qam"

Connected through hyperedge HE-7 (induced by source paper "arithmetic coding based multi composition codes for bit level distribution matching"):
  with members:
    - "shell mapping for distribution matching"
    - "divergence scaling of fixed length binary output one to one distribution matching"
    - "block to block distribution matching"

Connected through hyperedge HE-8 (induced by source paper "end to end learning for gmi optimized geometric constellation shape"):
  with members:
    - "increasing achievable information rates via geometric shaping"
    - "constellation shaping for wdm systems using 256qam 1024qam with probabilistic optimization"

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)