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 "graph neural networks for maximum constraint satisfaction"):
  Abstract: Many combinatorial optimization problems can be phrased in the language of constraint satisfaction problems. We introduce a graph neural network architecture for solving such optimization problems. The architecture is generic; it works for all binary constraint satisfaction problems. Training is unsupervised, and it is sufficient to train on relatively small instances; the resulting networks perform well on much larger instances (at least 10-times larger). We experimentally evaluate our approach for a variety of problems, including Maximum Cut and Maximum Independent Set. Despite being generic, we show that our approach matches or surpasses most greedy and semi-definite programming based algorithms and sometimes even outperforms state-of-the-art heuristics for the specific problems.

Includes members:
  Member "graph colouring meets deep learning effective graph neural network models for combinatorial problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "run csp unsupervised learning of message passing networks for binary constraint satisfaction problems")
  Member "learning to reason leveraging neural networks for approximate dnf counting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "graph neural networks meet neural symbolic computing a survey and perspective")
  Member "learning a sat solver from single bit supervision":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "teaching temporal logics to neural networks")
  Member "combinatorial optimization with graph convolutional networks and guided tree search":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "scalable kernelization for maximum independent sets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "finding all global minimum cuts in practice")
  Member "learning combinatorial optimization algorithms over graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lossless compression of deep neural networks")
  Member "finding near optimal independent sets at scale":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "solving np hard problems on graphs with extended alphago zero")
  Member "a simple model to generate hard satisfiable instances":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "it s not what machines can learn it s what we cannot teach")

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)