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: "constrained sampling and counting universal hashing meets sat solving"
  Abstract: Constrained sampling and counting are two fundamental problems in artificial intelligence with a diverse range of applications, spanning probabilistic reasoning and planning to constrained-random verification. While the theory of these problems was thoroughly investigated in the 1980s, prior work either did not scale to industrial size instances or gave up correctness guarantees to achieve scalability. Recently, we proposed a novel approach that combines universal hashing and SAT solving and scales to formulas with hundreds of thousands of variables without giving up correctness guarantees. This paper provides an overview of the key ingredients of the approach and discusses challenges that need to be overcome to handle larger real-world instances.

Connected through hyperedge HE-1 (induced by source paper "faster creation of smaller test suites with snap"):
  with members:
    - "data driven search based software engineering"
    - "hyperparameter optimization for effort estimation"
    - "riot a stochastic based method for workflow scheduling in the cloud"
    - "revisiting unsupervised learning for defect prediction"
    - "beyond evolutionary algorithms for search based software engineering"
    - "a survey of symbolic execution techniques"
    - "sampling as a baseline optimizer for search based software engineering"
    - "enhancing reuse of constraint solutions to improve symbolic execution"

Connected through hyperedge HE-2 (induced by source paper "weight learning in a probabilistic extension of answer set programs"):
  with members:
    - "a probabilistic extension of action language bc"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "probabilistic inductive logic programming based on answer set programming"

Connected through hyperedge HE-3 (induced by source paper "combining the k cnf and xor phase transitions"):
  with members:
    - "aspects of statistical physics in computational complexity"
    - "distribution aware sampling and weighted model counting for sat"
    - "balancing scalability and uniformity in sat witness generator"
    - "a scalable approximate model counter"
    - "a scalable and nearly uniform generator of sat witnesses"
    - "on the concentration of the number of solutions of random satisfiability formulas"

Connected through hyperedge HE-4 (induced by source paper "flexible constrained sampling with guarantees for pattern mining"):
  with members:
    - "distribution aware sampling and weighted model counting for sat"
    - "abacus frequent pattern mining based community discovery in multidimensional networks"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "a scalable and nearly uniform generator of sat witnesses"

Connected through hyperedge HE-5 (induced by source paper "computing scores of forwarding schemes in switched networks with probabilistic faults"):
  with members:
    - "software defined networking a comprehensive survey"
    - "distribution aware sampling and weighted model counting for sat"
    - "approximate counting in smt and value estimation for probabilistic programs"

Connected through hyperedge HE-6 (induced by source paper "the hard problems are almost everywhere for random cnf xor formulas"):
  with members:
    - "distribution aware sampling and weighted model counting for sat"
    - "a scalable and nearly uniform generator of sat witnesses"
    - "threshold values of random k sat from the cavity method"

Connected through hyperedge HE-7 (induced by source paper "towards verified artificial intelligence"):
  with members:
    - "systematic testing of convolutional neural networks for autonomous driving"
    - "compositional falsification of cyber physical systems with machine learning components"
    - "concrete problems in ai safety"
    - "diagnosis and repair for synthesis from signal temporal logic specifications"
    - "a theory of formal synthesis via inductive learning"
    - "analysis of classifiers robustness to adversarial perturbations"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "safe platooning of unmanned aerial vehicles via reachability"

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)