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 "solving np hard problems on graphs with extended alphago zero"):
  Abstract: There have been increasing challenges to solve combinatorial optimization problems by machine learning. Khalil et al. proposed an end-to-end reinforcement learning framework, S2V-DQN, which automatically learns graph embeddings to construct solutions to a wide range of problems. To improve the generalization ability of their Q-learning method, we propose a novel learning strategy based on AlphaGo Zero which is a Go engine that achieved a superhuman level without the domain knowledge of the game. Our framework is redesigned for combinatorial problems, where the final reward might take any real number instead of a binary response, win/lose. In experiments conducted for five kinds of NP-hard problems including {\sc MinimumVertexCover} and {\sc MaxCut}, our method is shown to generalize better to various graphs than S2V-DQN. Furthermore, our method can be combined with recently-developed graph neural network (GNN) models such as the \emph{Graph Isomorphism Network}, resulting in even better performance. This experiment also gives an interesting insight into a suitable choice of GNN models for each task.

Includes members:
  Member "universal invariant and equivariant graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "it s not what machines can learn it s what we cannot teach")
  Member "on the universality of invariant networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "provably powerful graph networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "elf opengo an analysis and open reimplementation of alphazero":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a hierarchy of limitations in machine learning")
  Member "combinatorial optimization with graph convolutional networks and guided tree search":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "neural message passing for quantum chemistry":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "learning combinatorial optimization algorithms over graphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "lossless compression of deep neural networks")

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.LG(Machine Learning)