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 "dynamic programming for discrete memoryless channel quantization"):
  Abstract: In this paper, we present a general framework for applying dynamic programming (DP) to discrete memoryless channel (DMC) quantization. The DP has complexity $O(q (N-M)^2 M)$, where $q$, $N$, and $M$ are alphabet sizes of the DMC input, DMC output, and the quantizer output, respectively. Then, starting from the quadrangle inequality (QI), we apply two techniques to reduce the DP's complexity. One technique makes use of the SMAWK algorithm with complexity $O(q (N-M) M)$, while the other technique is much easier to be implemented and has complexity $O(q (N^2 - M^2))$. Next, we give a sufficient condition on the channel transition probability, under which the two low-complexity techniques can be applied for designing quantizers that maximize the $\alpha$-mutual information, which is a generalized objective function for channel quantization. This condition works for the general $q$-ary input case, including the previous work for $q = 2$ as a subcase. Moreover, we propose a new idea, called iterative DP (IDP). Theoretical analysis and simulation results demonstrate that IDP can improve the quantizer design over the state-of-the-art methods in the literature.

Includes members:
  Member "mutual information maximizing quantized belief propagation decoding of ldpc codes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic programming for sequential deterministic quantization of discrete memoryless channels")
  Member "information theoretic bounds based channel quantization design for emerging memories":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep learning aided dynamic read thresholds design for multi level cell flash memories")
  Member "how to construct polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "polar codes and their quantum domain counterparts")
  Member "quantization of binary input discrete memoryless channels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")

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)