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: "graph neural networks and boolean satisfiability"
  Abstract: In this paper we explore whether or not deep neural architectures can learn to classify Boolean satisfiability (SAT). We devote considerable time to discussing the theoretical properties of SAT. Then, we define a graph representation for Boolean formulas in conjunctive normal form, and train neural classifiers over general graph structures called Graph Neural Networks, or GNNs, to recognize features of satisfiability. To the best of our knowledge this has never been tried before. Our preliminary findings are potentially profound. In a weakly-supervised setting, that is, without problem specific feature engineering, Graph Neural Networks can learn features of satisfiability.

Connected through hyperedge HE-1 (induced by source paper "meuzz smart seed scheduling for hybrid fuzzing"):
  with members:
    - "a review of machine learning applications in fuzzing"
    - "ptrix efficient hardware assisted fuzzing for cots binary"
    - "savior towards bug driven hybrid testing"
    - "learning a sat solver from single bit supervision"
    - "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage"
    - "evaluating fuzz testing"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "neuzz efficient fuzzing with neural program smoothing"

Connected through hyperedge HE-2 (induced by source paper "making logic learnable with neural networks"):
  with members:
    - "lutnet learning fpga configurations for highly efficient neural network inference"
    - "circuit based intrinsic methods to detect overfitting"
    - "learning a sat solver from single bit supervision"
    - "techniques for interpretable machine learning"
    - "fast obdd reordering using neural message passing on hypergraph"
    - "embedded binarized neural networks"
    - "finn a framework for fast scalable binarized neural network inference"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "improving graph attention networks with large margin based constraints"):
  with members:
    - "simplifying graph convolutional networks"
    - "multi hop reading comprehension across multiple documents by reasoning over heterogeneous graphs"
    - "a comprehensive survey on graph neural networks"
    - "link prediction based on graph neural networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "deeply learning molecular structure property relationships using attention and gate augmented graph convolutional network"
    - "geniepath graph neural networks with adaptive receptive paths"
    - "deeper insights into graph convolutional networks for semi supervised learning"

Connected through hyperedge HE-4 (induced by source paper "a review of machine learning applications in fuzzing"):
  with members:
    - "optimizing seed inputs in fuzzing with machine learning"
    - "deep reinforcement fuzzing"
    - "learning a sat solver from single bit supervision"
    - "evaluating fuzz testing"
    - "neuzz efficient fuzzing with neural program smoothing"
    - "angora efficient fuzzing by principled search"
    - "automatic heap layout manipulation for exploitation"
    - "deep reinforcement learning a brief survey"

Connected through hyperedge HE-5 (induced by source paper "automated proof synthesis for propositional logic with deep neural networks"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "can neural networks understand logical entailment"
    - "exploring generalization in deep learning"
    - "holstep a machine learning dataset for higher order logic theorem proving"
    - "towards proof synthesis guided by neural machine translation for intuitionistic propositional logic"
    - "a syntactic neural model for general purpose code generation"
    - "premise selection for theorem proving by deep graph embedding"
    - "deepmath deep sequence models for premise selection"

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.AI(Artificial Intelligence)