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: "optimal non uniform mapping for probabilistic shaping"
  Abstract: The construction of optimal non-uniform mappings for discrete input memoryless channels (DIMCs) is investigated. An efficient algorithm to find optimal mappings is proposed and the rate by which a target distribution is approached is investigated. The results are applied to non-uniform mappings for additive white Gaussian noise (AWGN) channels with finite signal constellations. The mappings found by the proposed methods outperform those obtained via a central limit theorem approach as suggested in the literature.

Connected through hyperedge HE-1 (induced by source paper "optimal quantization for distribution synthesis"):
  with members:
    - "greedy algorithms for optimal distribution approximation"
    - "f divergence inequalities"
    - "on reverse pinsker inequalities"
    - "renyi divergence and kullback leibler divergence"
    - "fixed to variable length resolution coding for target distributions"
    - "informational divergence approximations to product distributions"
    - "matching dyadic distributions to channels"

Connected through hyperedge HE-2 (induced by source paper "greedy algorithms for optimal distribution approximation"):
  with members:
    - "optimal quantization for distribution synthesis"
    - "constant composition distribution matching"

Connected through hyperedge HE-3 (induced by source paper "bandwidth efficient and rate matched low density parity check coded modulation"):
  with members:
    - "protograph based ldpc code design for bit metric decoding"
    - "arithmetic distribution matching"
    - "how to achieve the capacity of asymmetric channels"
    - "achieving awgn channel capacity with lattice gaussian coding"
    - "operating ldpc codes with zero shaping gap"
    - "constellation shaping for bit interleaved ldpc coded apsk"
    - "matching dyadic distributions to channels"

Connected through hyperedge HE-4 (induced by source paper "fixed to variable length resolution coding for target distributions"):
  with members:
    - "rooted trees with probabilities revisited"
    - "informational divergence approximations to product distributions"
    - "fixed to variable length distribution matching"
    - "strong coordination with polar codes"
    - "matching dyadic distributions to channels"

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)