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 "ai coding learning to construct error correction codes"):
  Abstract: In this paper, we investigate an artificial-intelligence (AI) driven approach to design error correction codes (ECC). Classic error correction code was designed upon coding theory that typically defines code properties (e.g., hamming distance, subchannel reliability, etc.) to reflect code performance. Its code design is to optimize code properties. However, an AI-driven approach doesn't necessarily rely on coding theory any longer. Specifically, we propose a constructor-evaluator framework, in which the code constructor is realized by AI algorithms and the code evaluator provides code performance metric measurements. The code constructor keeps improving the code construction to maximize code performance that is evaluated by the code evaluator. As examples, we construct linear block codes and polar codes with reinforcement learning (RL) and evolutionary algorithms. The results show that comparable code performance can be achieved with respect to the existing codes. It is noteworthy that our method can provide superior performances where existing classic constructions fail to achieve optimum for a specific decoder (e.g., list decoding for polar codes).

Includes members:
  Member "decoder tailored polar code design using the genetic algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "polar codes and their quantum domain counterparts")
  Member "deepcode feedback codes via deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "computing the feedback capacity of finite state channels using reinforcement learning")
  Member "parity check polar coding for 5g and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "polarization adjusted convolutional pac codes fano decoding vs list decoding")
  Member "an introduction to deep learning for the physical layer":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "beta expansion a theoretical framework for fast and recursive construction of polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rate flexible fast polar decoders")
  Member "algebraic properties of polar codes from a new polynomial formalism":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "polar decreasing monomial cartesian codes")
  Member "construction of polar codes with sublinear complexity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "decoder tailored polar code design using the genetic algorithm")
  Member "a rm polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "polarization adjusted convolutional pac codes fano decoding vs list decoding")

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)