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 "learning improvement heuristics for solving the travelling salesman problem"):
  Abstract: Recent studies in using deep learning to solve the Travelling Salesman Problem (TSP) focus on construction heuristics, the solution of which may still be far from optimality. To improve solution quality, additional procedures such as sampling or beam search are required. However, they are still based on the same construction policy, which is less effective in refining a solution. In this paper, we propose to directly learn the improvement heuristics for solving TSP based on deep reinforcement learning.We first present a reinforcement learning formulation for the improvement heuristic, where the policy guides selection of the next solution. Then, we propose a deep architecture as the policy network based on self-attention. Extensive experiments show that, improvement policies learned by our approach yield better results than state-of-the-art methods, even from random initial solutions. Moreover, the learned policies are more effective than the traditional hand-crafted ones, and robust to different initial solutions with either high or poor quality.

Includes members:
  Member "machine learning for combinatorial optimization a methodological tour d horizon":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lossless compression of deep neural networks")
  Member "learning the multiple traveling salesmen problem with permutation invariant pooling networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an efficient graph convolutional network technique for the travelling salesman problem")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "learning combinatorial optimization algorithms over graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lossless compression of deep neural networks")
  Member "time limits in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "artificial intelligence for prosthetics challenge solutions")
  Member "neural combinatorial optimization with reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "asynchronous methods for deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reinforcement learning for autonomous driving a survey")

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.AI(Artificial Intelligence)