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 "optimal quantization for distribution synthesis"):
  Abstract: Finite precision approximations of discrete probability distributions are considered, applicable for distribution synthesis, e.g., probabilistic shaping. Two algorithms are presented that find the optimal    $M$   -type approximation    $Q$    of a distribution    $P$    in terms of the variational distance    $\lVert Q-P\rVert _{1}$    and the informational divergence    $D(Q\Vert P)$   . Bounds on the approximation errors are derived and shown to be asymptotically tight. Several examples illustrate that the variational distance optimal approximation can be quite different from the informational divergence optimal approximation.

Includes members:
  Member "greedy algorithms for optimal distribution approximation":
    also connected through other hyperedges:
      (none)
  Member "f divergence inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on implicit regularization in beta vaes")
  Member "on reverse pinsker inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sample efficient model free reinforcement learning with off policy critics")
  Member "renyi divergence and kullback leibler divergence":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "fixed to variable length resolution coding for target distributions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "prefix free code distribution matching for probabilistic constellation shaping")
  Member "informational divergence approximations to product distributions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "communication for generating correlation a unifying survey")
  Member "optimal non uniform mapping for probabilistic shaping":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "greedy algorithms for optimal distribution approximation")
  Member "matching dyadic distributions to channels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rate constrained shaping codes for structured sources")

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)