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: "tangent automatic differentiation using source code transformation in python"
  Abstract: Automatic differentiation (AD) is an essential primitive for machine learning programming systems. Tangent is a new library that performs AD using source code transformation (SCT) in Python. It takes numeric functions written in a syntactic subset of Python and NumPy as input, and generates new Python functions which calculate a derivative. This approach to automatic differentiation is different from existing packages popular in machine learning, such as TensorFlow and Autograd. Advantages are that Tangent generates gradient code in Python which is readable by the user, easy to understand and debug, and has no runtime overhead. Tangent also introduces abstractions for easily injecting logic into the generated gradient code, further improving usability.

Connected through hyperedge HE-1 (induced by source paper "differentiable visual computing"):
  with members:
    - "on the convergence of adam and beyond"
    - "elimination of all bad local minima in deep learning"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "stochastic gradient descent with biased but consistent gradient estimators"
    - "line drawings from 3d models"
    - "tiramisu a polyhedral compiler for expressing fast and portable code"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"
    - "approximate program smoothing using mean variance statistics with application to procedural shader bandlimiting"

Connected through hyperedge HE-2 (induced by source paper "relay a high level ir for deep learning"):
  with members:
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "don t unroll adjoint differentiating ssa form programs"
    - "glow graph lowering compiler techniques for neural networks"
    - "darts differentiable architecture search"
    - "neural ordinary differential equations"
    - "demystifying differentiable programming shift reset the penultimate backpropagator"
    - "in datacenter performance analysis of a tensor processing unit"
    - "deep learning with dynamic computation graphs"

Connected through hyperedge HE-3 (induced by source paper "autograph imperative style coding with graph based performance"):
  with members:
    - "automatic differentiation in ml where we are and where we should be going"
    - "demystifying differentiable programming shift reset the penultimate backpropagator"
    - "precise dynamic information flow for database backed applications"
    - "theano a python framework for fast computation of mathematical expressions"
    - "tensorflow a system for large scale machine learning"
    - "improved semantic representations from tree structured long short term memory networks"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "julia a fast dynamic language for technical computing"

Connected through hyperedge HE-4 (induced by source paper "dynamic automatic differentiation of gpu broadcast kernels"):
  with members:
    - "confusion of tagged perturbations in forward automatic differentiation of higher order functions"
    - "effective extensible programming unleashing julia on gpus"
    - "don t unroll adjoint differentiating ssa form programs"
    - "automatic full compilation of julia programs and ml models to cloud tpus"
    - "demystifying differentiable programming shift reset the penultimate backpropagator"
    - "the simple essence of automatic differentiation"
    - "forward mode automatic differentiation in julia"
    - "hierarchical multiscale recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "automatic differentiation in ml where we are and where we should be going"):
  with members:
    - "tvm end to end optimization stack for deep learning"
    - "efficient implementation of a higher order language with built in ad"
    - "forward mode automatic differentiation in julia"
    - "diffsharp an ad library for net languages"
    - "tensorflow a system for large scale machine learning"
    - "deep learning in neural networks"
    - "improved semantic representations from tree structured long short term memory networks"
    - "automatic differentiation in machine learning a survey"

Connected through hyperedge HE-6 (induced by source paper "don t unroll adjoint differentiating ssa form programs"):
  with members:
    - "effective extensible programming unleashing julia on gpus"
    - "automatic full compilation of julia programs and ml models to cloud tpus"
    - "automatic differentiation in ml where we are and where we should be going"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "reversible architectures for arbitrarily deep residual neural networks"
    - "automatic differentiation in machine learning a survey"

Connected through hyperedge HE-7 (induced by source paper "automatic differentiation in machine learning a survey"):
  with members:
    - "confusion of tagged perturbations in forward automatic differentiation of higher order functions"
    - "divide and conquer checkpointing for arbitrary programs with no user annotation"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "online learning rate adaptation with hypergradient descent"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "tricks from deep learning"
    - "efficient implementation of a higher order language with built in ad"

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.MS(Mathematical Software)