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: "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).

Connected through hyperedge HE-1 (induced by source paper "design of capacity approaching low density parity check codes using recurrent neural networks"):
  with members:
    - "deep joint source channel coding for wireless image transmission"
    - "deep learning methods for improved decoding of linear codes"
    - "an introduction to deep learning for the physical layer"
    - "imposing hard constraints on deep networks promises and limitations"
    - "on deep learning based channel decoding"
    - "an overview of gradient descent optimization algorithms"
    - "adam a method for stochastic optimization"
    - "practical recommendations for gradient based training of deep architectures"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning for nested polar code construction"):
  with members:
    - "decoder in the loop genetic optimization based ldpc code design"
    - "decoder tailored polar code design using the genetic algorithm"
    - "reinforcement learning from imperfect demonstrations"
    - "deepcode feedback codes via deep learning"
    - "pretraining deep actor critic reinforcement learning algorithms with expert demonstrations"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "proximal policy optimization algorithms"
    - "high dimensional continuous control using generalized advantage estimation"

Connected through hyperedge HE-3 (induced by source paper "encoders and decoders for quantum expander codes using machine learning"):
  with members:
    - "on deep learning based channel decoding"
    - "deep exploration via bootstrapped dqn"
    - "boolean functions projection operators and quantum error correcting codes"

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)