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: "shell mapping for distribution matching"
  Abstract: Distribution matching transforms independent and Bernoulli(1/2) distributed input bits into a sequence of output symbols with a desired distribution PA. Fixed-to-fixed length, invertible encoders and decoders based on shell mapping are presented. The matcher minimizes the informational divergence for a large class of distributions including the Maxwell-Boltzmann and geometric distributions. Finite length simulations show performance gains of up to 0.6 dB as compared to a constant composition distribution matching for short matcher output lengths.

Connected through hyperedge HE-1 (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"
    - "experimental demonstration of capacity increase and rate adaptation by probabilistically shaped 64 qam"
    - "constant composition distribution matching"

Connected through hyperedge HE-2 (induced by source paper "arithmetic coding based multi composition codes for bit level distribution matching"):
  with members:
    - "divergence scaling of fixed length binary output one to one distribution matching"
    - "constant composition distribution matching"
    - "block to block distribution matching"

Connected through hyperedge HE-3 (induced by source paper "prefix free code distribution matching for probabilistic constellation shaping"):
  with members:
    - "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"
    - "matching dyadic distributions to channels"
    - "prefix codes equiprobable words unequal letter costs"

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)