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: "learning a sat solver from single bit supervision"
  Abstract: We present NeuroSAT, a message passing neural network that learns to solve SAT problems after only being trained as a classifier to predict satisfiability. Although it is not competitive with state-of-the-art SAT solvers, NeuroSAT can solve problems that are substantially larger and more difficult than it ever saw during training by simply running for more iterations. Moreover, NeuroSAT generalizes to novel distributions; after training only on random SAT problems, at test time it can solve SAT problems encoding graph coloring, clique detection, dominating set, and vertex cover problems, all on a range of distributions over small random graphs.

Connected through hyperedge HE-1 (induced by source paper "teaching temporal logics to neural networks"):
  with members:
    - "mathematical reasoning in latent space"
    - "guiding high performance sat solvers with unsat core predictions"
    - "enhancing the transformer with explicit relational encoding for math problem solving"
    - "analysing mathematical reasoning abilities of neural models"
    - "deep learning for symbolic mathematics"
    - "graph representations for higher order logic and theorem proving"
    - "holist an environment for machine learning of higher order theorem proving"
    - "a comprehensive survey on graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "graph neural networks meet neural symbolic computing a survey and perspective"):
  with members:
    - "the next decade in ai four steps towards robust artificial intelligence"
    - "run csp unsupervised learning of message passing networks for binary constraint satisfaction problems"
    - "recurrent independent mechanisms"
    - "deep learning for symbolic mathematics"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "neural symbolic computing an effective methodology for principled integration of machine learning and reasoning"
    - "the use of deep learning for symbolic integration a review of lample and charton 2019"
    - "learning to reason leveraging neural networks for approximate dnf counting"

Connected through hyperedge HE-3 (induced by source paper "it s not what machines can learn it s what we cannot teach"):
  with members:
    - "mipaal mixed integer program as a layer"
    - "generating difficult sat instances by preventing triangles"
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "hidden stratification causes clinically meaningful failures in machine learning for medical imaging"
    - "guiding high performance sat solvers with unsat core predictions"
    - "a survey on bias and fairness in machine learning"
    - "deep learning for symbolic mathematics"
    - "the use of deep learning for symbolic integration a review of lample and charton 2019"

Connected through hyperedge HE-4 (induced by source paper "flexible and context specific ai explainability a multidisciplinary approach"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "functional transparency for structured data a game theoretic approach"
    - "towards robust locally linear deep networks"
    - "explaining a black box by using a deep variational information bottleneck approach"
    - "safely entering the deep a review of verification and validation for machine learning and a challenge elicitation in the automotive industry"
    - "contextual explanation networks"
    - "smoothgrad removing noise by adding noise"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"

Connected through hyperedge HE-5 (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"
    - "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"
    - "angora efficient fuzzing by principled search"

Connected through hyperedge HE-6 (induced by source paper "a deep reinforcement learning algorithm using dynamic attention model for vehicle routing problems"):
  with members:
    - "learning heuristics over large graphs via deep reinforcement learning"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "learning to solve np complete problems a graph neural network for decision tsp"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "attention is all you need"
    - "neural message passing for quantum chemistry"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "learning algebraic multigrid using graph neural networks"):
  with members:
    - "learning neural pde solvers with convergence guarantees"
    - "on the equivalence between graph isomorphism testing and function approximation with gnns"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "relational inductive biases deep learning and graph networks"
    - "dynamic graph cnn for learning on point clouds"
    - "neural message passing for quantum chemistry"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-8 (induced by source paper "graph neural networks for maximum constraint satisfaction"):
  with members:
    - "graph colouring meets deep learning effective graph neural network models for combinatorial problems"
    - "learning to reason leveraging neural networks for approximate dnf counting"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "scalable kernelization for maximum independent sets"
    - "learning combinatorial optimization algorithms over graphs"
    - "finding near optimal independent sets at scale"
    - "a simple model to generate hard satisfiable instances"
    - "many hard examples in exact phase transitions with application to generating hard satisfiable instances"

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)