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: "neural network gradient based learning of black box function interfaces"
  Abstract: Deep neural networks work well at approximating complicated functions when provided with data and trained by gradient descent methods. At the same time, there is a vast amount of existing functions that programmatically solve different tasks in a precise manner eliminating the need for training. In many cases, it is possible to decompose a task to a series of functions, of which for some we may prefer to use a neural network to learn the functionality, while for others the preferred method would be to use existing black-box functions. We propose a method for end-to-end training of a base neural network that integrates calls to existing black-box functions. We do so by approximating the black-box functionality with a differentiable neural network in a way that drives the base network to comply with the black-box function interface during the end-to-end optimization process. At inference time, we replace the differentiable estimator with its external black-box non-differentiable counterpart such that the base network output matches the input arguments of the black-box function. Using this "Estimate and Replace" paradigm, we train a neural network, end to end, to compute the input to black-box functionality while eliminating the need for intermediate labels. We show that by leveraging the existing precise black-box function during inference, the integrated model generalizes better than a fully differentiable model, and learns more efficiently compared to RL-based methods.

Connected through hyperedge HE-1 (induced by source paper "rest performance improvement of a black box model via rl based spatial transformation"):
  with members:
    - "distort and recover color enhancement using deep reinforcement learning"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "pointwise rotation invariant network with adaptive sampling and 3d spherical voxel convolution"
    - "collaborative deep reinforcement learning for joint object search"
    - "dynamic routing between capsules"
    - "deformable convolutional networks"
    - "proximal policy optimization algorithms"
    - "a2 rl aesthetics aware reinforcement learning for image cropping"

Connected through hyperedge HE-2 (induced by source paper "stealing black box functionality using the deep neural tree architecture"):
  with members:
    - "a survey on explainable artificial intelligence xai towards medical xai"
    - "knockoff nets stealing functionality of black box models"
    - "neural arithmetic logic units"
    - "hyperbolic neural networks"
    - "query efficient black box attack by active learning"
    - "adaptive neural trees"
    - "stealing hyperparameters in machine learning"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-3 (induced by source paper "inversynth deep estimation of synthesizer parameter configurations from audio signals"):
  with members:
    - "deep synthesizer parameter estimation"
    - "exposure a white box photo post processing framework"
    - "bayesian neural word embedding"
    - "automatic tagging using deep convolutional neural networks"
    - "wavenet a generative model for raw audio"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-4 (induced by source paper "learning representations by humans for humans"):
  with members:
    - "the algorithmic automation problem prediction triage and human effort"
    - "manipulating and measuring model interpretability"
    - "smoothgrad removing noise by adding noise"
    - "interventions over predictions reframing the ethical debate for actuarial risk assessment"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "rationalizing neural predictions"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"

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)