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: "recursive decomposition for nonconvex optimization"
  Abstract: Continuous optimization is an important problem in many areas of AI, including vision, robotics, probabilistic inference, and machine learning. Unfortunately, most real-world optimization problems are nonconvex, causing standard convex techniques to find only local optima, even with extensions like random restarts and simulated annealing. We observe that, in many cases, the local modes of the objective function have combinatorial structure, and thus ideas from combinatorial optimization can be brought to bear. Based on this, we propose a problem-decomposition approach to nonconvex optimization. Similarly to DPLL-style SAT solvers and recursive conditioning in probabilistic inference, our algorithm, RDIS, recursively sets variables so as to simplify and decompose the objective function into approximately independent sub-functions, until the remaining functions are simple enough to be optimized by standard techniques like gradient descent. The variables to set are chosen by graph partitioning, ensuring decomposition whenever possible. We show analytically that RDIS can solve a broad class of nonconvex optimization problems exponentially faster than gradient descent with random restarts. Experimentally, RDIS outperforms standard techniques on problems like structure from motion and protein folding.

Connected through hyperedge HE-1 (induced by source paper "semiring programming a declarative framework for generalized sum product problems"):
  with members:
    - "reasoning about discrete and continuous noisy sensors and effectors in dynamical systems"
    - "the sum product theorem a foundation for learning tractable models"
    - "stable model counting and its application in probabilistic logic programming"
    - "optimization modulo theories with linear rational costs"
    - "on the complexity of optimization problems based on compiled nnf representations"
    - "approximate counting in smt and value estimation for probabilistic programs"
    - "algebraic model counting"
    - "inference in probabilistic logic programs using weighted cnf s"

Connected through hyperedge HE-2 (induced by source paper "turning high dimensional optimization into computationally expensive optimization"):
  with members:
    - "negatively correlated search"
    - "toward the coevolution of novel vertical axis wind turbines"
    - "scaling up estimation of distribution algorithms for continuous optimization"

Connected through hyperedge HE-3 (induced by source paper "exploiting problem structure in combinatorial landscapes a case study on pure mathematics application"):
  with members:
    - "phase transitions and backbones of the asymmetric traveling salesman problem"
    - "when gravity fails local search topology"

Connected through hyperedge HE-4 (induced by source paper "deep learning as a mixed convex combinatorial optimization problem"):
  with members:
    - "training quantized nets a deeper understanding"
    - "failures of gradient based deep learning"
    - "mixed precision training"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "training neural networks without gradients a scalable admm approach"
    - "the sum product theorem a foundation for learning tractable models"
    - "trained ternary quantization"
    - "fixed point quantization of deep convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "the sum product theorem a foundation for learning tractable models"):
  with members:
    - "a differential approach to inference in bayesian networks"
    - "value elimination bayesian inference via backtracking search"
    - "algebraic model counting"
    - "sum product networks a new deep architecture"
    - "the relationship between and or search and variable elimination"
    - "complexity of inference in graphical models"
    - "solving sat and bayesian inference with backtracking search"
    - "the language of search"

Connected through hyperedge HE-6 (induced by source paper "semiring programming a framework for search inference and learning"):
  with members:
    - "optimization modulo theories with linear rational costs"
    - "on the complexity of optimization problems based on compiled nnf representations"
    - "approximate counting in smt and value estimation for probabilistic programs"
    - "algebraic model counting"
    - "inference in probabilistic logic programs using weighted cnf s"
    - "church a language for generative models"
    - "probabilistic reasoning with answer sets"

Connected through hyperedge HE-7 (induced by source paper "the symbolic interior point method"):
  with members:
    - "spudd stochastic planning using decision diagrams"
    - "first order mixed integer linear programming"

Connected through hyperedge HE-8 (induced by source paper "variable elimination in the fourier domain"):
  with members:
    - "structured message passing"
    - "a complete anytime algorithm for treewidth"
    - "on the partition function and random maximum a posteriori perturbations"
    - "tightening lp relaxations for map using message passing"
    - "join graph based cost shifting schemes"
    - "join graph propagation algorithms"
    - "a knowledge compilation map"

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)