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: "a simple model to generate hard satisfiable instances"
  Abstract: In this paper, we try to further demonstrate that the models of random CSP instances proposed by [Xu and Li, 2000; 2003] are of theoretical and practical interest. Indeed, these models, called RB and RD, present several nice features. First, it is quite easy to generate random instances of any arity since no particular structure has to be integrated, or property enforced, in such instances. Then, the existence of an asymptotic phase transition can be guaranteed while applying a limited restriction on domain size and on constraint tightness. In that case, a threshold point can be precisely located and all instances have the guarantee to be hard at the threshold, i.e., to have an exponential tree-resolution complexity. Next, a formal analysis shows that it is possible to generate forced satisfiable instances whose hardness is similar to unforced satisfiable ones. This analysis is supported by some representative results taken from an intensive experimentation that we have carried out, using complete and incomplete search methods.

Connected through hyperedge HE-1 (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-2 (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"
    - "learning a sat solver from single bit supervision"
    - "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"
    - "many hard examples in exact phase transitions with application to generating hard satisfiable instances"

Connected through hyperedge HE-3 (induced by source paper "improving the performance of stochastic local search for maximum vertex weight clique problem using programming by optimization"):
  with members:
    - "the configurable sat solver challenge cssc"
    - "dynamic local search for the maximum clique problem"

Connected through hyperedge HE-4 (induced by source paper "g2sat learning to generate sat formulas"):
  with members:
    - "position aware graph neural networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "learning a sat solver from single bit supervision"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "junction tree variational autoencoder for molecular graph generation"
    - "representation learning on graphs methods and applications"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "numvc an efficient local search algorithm for minimum vertex cover"):
  with members:
    - "new inference rules for max sat"
    - "dynamic local search for the maximum clique problem"
    - "a novel evolutionary formulation of the maximum independent set problem"
    - "optimal schedules for parallelizing anytime algorithms the case of shared resources"
    - "exact phase transitions in random constraint satisfaction problems"

Connected through hyperedge HE-6 (induced by source paper "new inference rules for max sat"):
  with members:
    - "a logical approach to efficient max sat solving"
    - "arc consistency for soft constraints"

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)