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: "developing bug free machine learning systems with formal mathematics"
  Abstract: Noisy data, non-convex objectives, model misspecification, and numerical instability can all cause undesired behaviors in machine learning systems. As a result, detecting actual implementation errors can be extremely difficult. We demonstrate a methodology in which developers use an interactive proof assistant to both implement their system and to state a formal theorem defining what it means for their system to be correct. The process of proving this theorem interactively in the proof assistant exposes all implementation errors since any error in the program would cause the proof to fail. As a case study, we implement a new system, Certigrad, for optimizing over stochastic computation graphs, and we generate a formal (i.e. machine-checkable) proof that the gradients sampled by the system are unbiased estimates of the true mathematical gradients. We train a variational autoencoder using Certigrad and find the performance comparable to training the same model in TensorFlow.

Connected through hyperedge HE-1 (induced by source paper "a simple differentiable programming language"):
  with members:
    - "pytorch an imperative style high performance deep learning library"
    - "efficient differentiable programming in a functional array processing language"
    - "demystifying differentiable programming shift reset the penultimate backpropagator"
    - "dynamic control flow in large scale machine learning"
    - "the simple essence of automatic differentiation"
    - "tangent automatic differentiation using source code transformation for dynamically typed array programming"
    - "tricks from deep learning"
    - "a convenient differential category"

Connected through hyperedge HE-2 (induced by source paper "towards identifying and closing gaps in assurance of autonomous road vehicles a collection of technical notes part 1"):
  with members:
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "sok security and privacy in machine learning"
    - "toward scalable verification for safety critical deep networks"
    - "motivating the rules of the game for adversarial example research"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "safety verification of deep neural networks"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-3 (induced by source paper "assuring the machine learning lifecycle desiderata methods and challenges"):
  with members:
    - "example and feature importance based explanations for black box machine learning models"
    - "back to basics benchmarking canonical evolution strategies for playing atari"
    - "visual interpretability for deep learning a survey"
    - "controlling over generalization and its effect on adversarial examples detection and generation"
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "counterexample guided data augmentation"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "relay a high level compiler for deep learning"):
  with members:
    - "autograph imperative style coding with graph based performance"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "glow graph lowering compiler techniques for neural networks"
    - "a hardware software blueprint for flexible deep learning specialization"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "demystifying differentiable programming shift reset the penultimate backpropagator"

Connected through hyperedge HE-5 (induced by source paper "metamorphic testing of a deep learning based forecaster"):
  with members:
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "the elephant in the room"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "exploring the landscape of spatial robustness"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"
    - "deeptest automated testing of deep neural network driven autonomous cars"

Connected through hyperedge HE-6 (induced by source paper "a categorisation of post hoc explanations for predictive models"):
  with members:
    - "this looks like that deep learning for interpretable image recognition"
    - "consistent individualized feature attribution for tree ensembles"
    - "bayesian patchworks an approach to case based reasoning"
    - "global model interpretation via recursive partitioning"
    - "interpretable explorable approximations of black box models"
    - "explanation in artificial intelligence insights from the social sciences"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "using program induction to interpret transition system dynamics"

Connected through hyperedge HE-7 (induced by source paper "a formal proof of pac learnability for decision stumps"):
  with members:
    - "guiding high performance sat solvers with unsat core predictions"
    - "gamepad a learning environment for theorem proving"
    - "holstep a machine learning dataset for higher order logic theorem proving"
    - "on the expressive power of deep learning a tensor analysis"
    - "the foundational cryptography framework"

Connected through hyperedge HE-8 (induced by source paper "tfcheck a tensorflow library for detecting training issues in neural network programs"):
  with members:
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "on testing machine learning programs"
    - "testing mcmc code"

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.SE(Software Engineering)