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 distribution matching"
  Abstract: Fixed-to-variable length (f2v) matchers are used to reversibly transform an input sequence of independent and uniformly distributed bits into an output sequence of bits that are (approximately) independent and distributed according to a target distribution. The degree of approximation is measured by the informational divergence between the output distribution and the target distribution. An algorithm is developed that efficiently finds optimal f2v codes. It is shown that by encoding the input bits blockwise, the informational divergence per bit approaches zero as the block length approaches infinity. A relation to data compression by Tunstall coding is established.

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"
    - "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 "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 "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"
    - "effective secrecy reliability confusion and stealth"
    - "operating ldpc codes with zero shaping gap"
    - "matching dyadic distributions to channels"

Connected through hyperedge HE-5 (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"
    - "operating ldpc codes with zero shaping gap"
    - "matching dyadic distributions to channels"

Connected through hyperedge HE-6 (induced by source paper "constant composition distribution matching"):
  with members:
    - "optimal quantization for distribution synthesis"
    - "arithmetic distribution matching"
    - "how to achieve the capacity of asymmetric channels"
    - "block to block distribution matching"
    - "informational divergence and entropy rate on rooted trees with probabilities"
    - "operating ldpc codes with zero shaping gap"
    - "matching dyadic distributions to channels"

Connected through hyperedge HE-7 (induced by source paper "arithmetic distribution matching"):
  with members:
    - "probabilistic signal shaping for bit metric decoding"
    - "informational divergence and entropy rate on rooted trees with probabilities"
    - "matching dyadic distributions to channels"

Connected through hyperedge HE-8 (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"
    - "strong coordination with polar codes"
    - "optimal non uniform mapping for probabilistic shaping"
    - "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)