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: "fixed to variable length resolution coding for target distributions"
  Abstract: The number of random bits required to approximate a target distribution in terms of un-normalized informational divergence is considered. It is shown that for a variable-to-variable length encoder, this number is lower bounded by the entropy of the target distribution. A fixed-to-variable length encoder is constructed using M-type quantization and Tunstall coding. It is shown that the encoder achieves in the limit an un-normalized informational divergence of zero with the number of random bits per generated symbol equal to the entropy of the target distribution. Numerical results show that the proposed encoder significantly outperforms the optimal block-to-block encoder in the finite length regime.

Connected through hyperedge HE-1 (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"
    - "constellation shaping for bit interleaved ldpc coded apsk"
    - "matching dyadic distributions to channels"
    - "prefix codes equiprobable words unequal letter costs"

Connected through hyperedge HE-2 (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"
    - "informational divergence approximations to product distributions"
    - "optimal non uniform mapping for probabilistic shaping"
    - "matching dyadic distributions to channels"

Connected through hyperedge HE-3 (induced by source paper "informational divergence and entropy rate on rooted trees with probabilities"):
  with members:
    - "fixed to variable length distribution matching"
    - "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)