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: "phase transitions and backbones of the asymmetric traveling salesman problem"
  Abstract: In recent years, there has been much interest in phase transitions of combinatorial problems. Phase transitions have been successfully used to analyze combinatorial optimization problems, characterize their typical-case features and locate the hardest problem instances. In this paper, we study phase transitions of the asymmetric Traveling Salesman Problem (ATSP), an NP-hard combinatorial optimization problem that has many real-world applications. Using random instances of up to 1,500 cities in which intercity distances are uniformly distributed, we empirically show that many properties of the problem, including the optimal tour cost and backbone size, experience sharp transitions as the precision of intercity distances increases across a critical value. Our experimental results on the costs of the ATSP tours and assignment problem agree with the theoretical result that the asymptotic cost of assignment problem is π26 as the number of cities goes to infinity. In addition, we show that the average computational cost of the well-known branch-and-bound subtour elimination algorithm for the problem also exhibits a thrashing behavior, transitioning from easy to difficult as the distance precision increases. These results answer positively an open question regarding the existence of phase transitions in the ATSP, and provide guidance on how difficult ATSP problem instances should be generated.

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 "learning to solve np complete problems a graph neural network for decision tsp"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "relational inductive biases deep learning and graph networks"
    - "learning explanatory rules from noisy data"
    - "recurrent relational networks"
    - "neural message passing for quantum chemistry"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "exploiting problem structure in combinatorial landscapes a case study on pure mathematics application"):
  with members:
    - "recursive decomposition for nonconvex optimization"
    - "when gravity fails local search topology"

Connected through hyperedge HE-4 (induced by source paper "pattern based approach to the workflow satisfiability problem with user independent constraints"):
  with members:
    - "algorithms for the workflow satisfiability problem engineered for counting constraints"
    - "the bi objective workflow satisfiability problem and workflow resiliency"
    - "tight lower bounds for the workflow satisfiability problem based on the strong exponential time hypothesis"
    - "valued workflow satisfiability problem"
    - "polynomial kernels and user reductions for the workflow satisfiability problem"
    - "iterative plan construction for the workflow satisfiability problem"
    - "parameterized complexity results for exact bayesian network structure learning"
    - "on the parameterized complexity and kernelization of the workflow satisfiability problem"

Connected through hyperedge HE-5 (induced by source paper "is computational complexity a barrier to manipulation"):
  with members:
    - "dominating manipulations in voting with partial information"
    - "manipulation of nanson s and baldwin s rules"
    - "complexity of and algorithms for borda manipulation"
    - "an empirical study of borda manipulation"
    - "multimode control attacks on elections"
    - "an empirical study of the manipulability of single transferable voting"
    - "where are the hard manipulation problems"
    - "manipulation and gender neutrality in stable marriage procedures"

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)