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: "confusion of tagged perturbations in forward automatic differentiation of higher order functions"
  Abstract: Forward Automatic Differentiation (AD) is a technique for augmenting programs to both perform their original calculation and also compute its directional derivative. The essence of Forward AD is to attach a derivative value to each number, and propagate these through the computation. When derivatives are nested, the distinct derivative calculations, and their associated attached values, must be distinguished. In dynamic languages this is typically accomplished by creating a unique tag for each application of the derivative operator, tagging the attached values, and overloading the arithmetic operators. We exhibit a subtle bug, present in fielded implementations, in which perturbations are confused despite the tagging machinery.

Connected through hyperedge HE-1 (induced by source paper "on the versatility of open logical relations continuity automatic differentiation and a containment theorem"):
  with members:
    - "a simple differentiable programming language"
    - "efficient differentiable programming in a functional array processing language"
    - "the simple essence of automatic differentiation"
    - "automatic differentiation in machine learning a survey"
    - "step indexed logical relations for probability"
    - "abstract effects and proof relevant logical relations"
    - "church scott ptime an application of resource sensitive realizability"
    - "logical relations for monadic types"

Connected through hyperedge HE-2 (induced by source paper "dynamic automatic differentiation of gpu broadcast kernels"):
  with members:
    - "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"
    - "tangent automatic differentiation using source code transformation in python"
    - "forward mode automatic differentiation in julia"
    - "hierarchical multiscale recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "efficient implementation of a higher order language with built in ad"):
  with members:
    - "ad in fortran part 1 design"
    - "ad in fortran implementation via prepreprocessor"

Connected through hyperedge HE-4 (induced by source paper "evolving the incremental λ calculus into a model of forward automatic differentiation ad"):
  with members:
    - "a theory of changes for higher order languages incrementalizing lambda calculi by static differentiation"

Connected through hyperedge HE-5 (induced by source paper "automatic differentiation in machine learning a survey"):
  with members:
    - "divide and conquer checkpointing for arbitrary programs with no user annotation"
    - "tangent automatic differentiation using source code transformation in python"
    - "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.SC(Symbolic Computation)