human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "rate constrained shaping codes for structured sources"):
  Abstract: Shaping codes are used to encode information for use on channels with cost constraints. Applications include data transmission with a power constraint and, more recently, data storage on flash memories with a constraint on memory cell wear. In the latter application, system requirements often impose a rate constraint. In this paper, we study rate-constrained fixed-to-variable length shaping codes for noiseless, memoryless costly channels and general i.i.d. sources. The analysis relies on the theory of word-valued sources. We establish a relationship between the code expansion factor and minimum average symbol cost. We then determine the expansion factor that minimizes the average cost per source symbol (total cost), corresponding to a conventional optimal source code with cost. An equivalence is established between codes minimizing average symbol cost and codes minimizing total cost, and a separation theorem is proved, showing that optimal shaping can be achieved by a concatenation of optimal compression and optimal shaping for a uniform i.i.d. source. Shaping codes often incorporate, either explicitly or implicitly, some form of non-equiprobable signaling. We use our results to further explore the connections between shaping codes and codes that map a sequence of i.i.d. source symbols into an output sequence of symbols that are approximately independent and distributed according to a specified target distribution, such as distribution matching (DM) codes. Optimal DM codes are characterized in terms of a new performance measure - generalized expansion factor (GEF) - motivated by the costly channel perspective. The GEF is used to study DM codes that minimize informational divergence and normalized informational divergence.

Includes members:
  Member "divergence optimal fixed to fixed length distribution matching with shell mapping":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "introducing enumerative sphere shaping for optical communication systems with short blocklengths")
  Member "performance of optimal data shaping codes":
    also connected through other hyperedges:
      (none)
  Member "constant composition distribution matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on product codes with probabilistic amplitude shaping for high throughput fiber optic systems")
  Member "arithmetic distribution matching":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on product codes with probabilistic amplitude shaping for high throughput fiber optic systems")
  Member "how to achieve the capacity of asymmetric channels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "explicit codes for the wiretap channel a unified design framework")
  Member "fixed to variable length distribution matching":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on product codes with probabilistic amplitude shaping for high throughput fiber optic systems")
  Member "informational divergence and entropy rate on rooted trees with probabilities":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "information rates and error exponents for probabilistic amplitude shaping")
  Member "matching dyadic distributions to channels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on product codes with probabilistic amplitude shaping for high throughput fiber optic systems")

Question: Please tell me which class the source paper 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)