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: "the sum product theorem a foundation for learning tractable models"
  Abstract: Inference in expressive probabilistic models is generally intractable, which makes them difficult to learn and limits their applicability. Sum-product networks are a class of deep models where, surprisingly, inference remains tractable even when an arbitrary number of hidden layers are present. In this paper, we generalize this result to a much broader set of learning problems: all those where inference consists of summing a function over a semiring. This includes satisfiability, constraint satisfaction, optimization, integration, and others. In any semiring, for summation to be tractable it suffices that the factors of every product have disjoint scopes. This unifies and extends many previous results in the literature. Enforcing this condition at learning time thus ensures that the learned models are tractable. We illustrate the power and generality of this approach by applying it to a new type of structured prediction problem: learning a nonconvex function that can be globally optimized in polynomial time. We show empirically that this greatly outperforms the standard approach of learning without regard to the cost of optimization.

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"
    - "recursive decomposition for nonconvex optimization"
    - "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 "smoothing structured decomposable circuits"):
  with members:
    - "approximate knowledge compilation by online collapsed importance sampling"
    - "on relaxing determinism in arithmetic circuits"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "maximum a posteriori inference in sum product networks"
    - "on the latent variable interpretation in sum product networks"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "expander cnfs have exponential dnnf size"
    - "skolemization for weighted first order model counting"

Connected through hyperedge HE-3 (induced by source paper "integrity constraints revisited from exact to approximate implication"):
  with members:
    - "bias in olap queries detection explanation and removal"
    - "conditional information inequalities for entropic and almost entropic points"
    - "sum product networks a new deep architecture"
    - "linear rank inequalities on five or more variables"

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"
    - "recursive decomposition for nonconvex optimization"
    - "training neural networks without gradients a scalable admm approach"
    - "trained ternary quantization"
    - "fixed point quantization of deep convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "on relaxing determinism in arithmetic circuits"):
  with members:
    - "a differential approach to inference in bayesian networks"
    - "algebraic model counting"
    - "learning arithmetic circuits"
    - "sum product networks a new deep architecture"
    - "a knowledge compilation map"

Connected through hyperedge HE-6 (induced by source paper "an algebraic formalization of forward and forward backward algorithms"):
  with members:
    - "case factor diagrams for structured probabilistic modeling"

Connected through hyperedge HE-7 (induced by source paper "on the latent variable interpretation in sum product networks"):
  with members:
    - "a unified approach for learning the parameters of sum product networks"
    - "on the relationship between sum product networks and bayesian networks"
    - "a bayesian approach to learning bayesian networks with local structure"
    - "a differential approach to inference in bayesian networks"
    - "context specific independence in bayesian networks"
    - "learning arithmetic circuits"
    - "sum product networks a new deep architecture"
    - "map complexity results and approximation methods"

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.LG(Machine Learning)