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: "estimating or propagating gradients through stochastic neurons"
  Abstract: Stochastic neurons can be useful for a number of reasons in deep learning models, but in many cases they pose a challenging problem: how to estimate the gradient of a loss function with respect to the input of such s tochastic neurons, i.e., can we “back-propagate” through these stochastic neurons? We examine this question, existing approaches, and present two novel families of solutions, applicable in different settings. In particular, it is demonstrate d that a simple biologically plausible formula gives rise to an an unbiased (but noisy) estimator of the gradient with respect to a binary stochastic neuron firing proba bility. Unlike other estimators which view the noise as a small perturbation in order to estimate gradients by finite differences, this estimator is unbiased even w ithout assuming that the stochastic perturbation is small. This estimator is also in teresting because it can be applied in very general settings which do not allow gradient back-propagation, including the estimation of the gradient with respect to futur e rewards, as required in reinforcement learning setups. We also propose an approach to approximating this unbiased but high-variance estimator by learning to predict it using a biased estimator. The second approach we propose assumes that an estimator of the gradient can be back-propagated and it provides an unbiased estimator of the gradient, but can only work with non-linearities unlike the hard threshold, but like the rectifier, that are not flat for all of their range. This is similar to trad itional sigmoidal units but has the advantage that for many inputs, a hard decision (e.g., a 0 output) can be produced, which would be convenient for conditional computation and achieving sparse representations and sparse gradients.

Connected through hyperedge HE-1 (induced by source paper "prnet self supervised learning for partial to partial registration"):
  with members:
    - "a polynomial time solution for robust registration with extreme outlier rates"
    - "kpconv flexible and deformable convolution for point clouds"
    - "deep part induction from articulated object pairs"
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "deep closest point learning representations for point cloud registration"
    - "rethinking imagenet pre training"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"

Connected through hyperedge HE-2 (induced by source paper "learning latent trees with stochastic perturbations and differentiable dynamic programming"):
  with members:
    - "listops a diagnostic dataset for latent tree learning"
    - "backpropagating through structured argmax using a spigot"
    - "towards dynamic computation graphs via sparse latent structure"
    - "a tree based decoder for neural machine translation"
    - "differentiable perturb and parse semi supervised parsing with a structured variational autoencoder"
    - "grammar induction with neural language models an unusual replication"
    - "structured attention networks"
    - "learning structured text representations"

Connected through hyperedge HE-3 (induced by source paper "deep learning for distributed optimization applications to wireless resource management"):
  with members:
    - "learning optimal resource allocations in wireless systems"
    - "machine learning in the air"
    - "learning to optimize training deep neural networks for interference management"
    - "end to end deep learning of optical fiber communications"
    - "deep learning framework for wireless systems applications to optical wireless communications"
    - "an introduction to deep learning for the physical layer"
    - "optnet differentiable optimization as a layer in neural networks"
    - "the expressive power of neural networks a view from the width"

Connected through hyperedge HE-4 (induced by source paper "a deep learning approach to universal binary visible light communication transceiver"):
  with members:
    - "end to end deep learning of optical fiber communications"
    - "deep learning framework for wireless systems applications to optical wireless communications"
    - "model free training of end to end communication systems"
    - "an introduction to deep learning for the physical layer"
    - "an efficient flicker free fec coding scheme for dimmable visible light communication based on polar codes"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"

Connected through hyperedge HE-5 (induced by source paper "dfternet towards 2 bit dynamic fusion networks for accurate human activity recognition"):
  with members:
    - "learning attribute representation for human activity recognition"
    - "faster population counts using avx2 instructions"
    - "network sketching exploiting binary structure in deep cnns"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "qsgd communication efficient sgd via gradient quantization and encoding"

Connected through hyperedge HE-6 (induced by source paper "sgad soft guided adaptively dropped neural network"):
  with members:
    - "skipnet learning dynamic routing in convolutional networks"
    - "adaptive neural networks for efficient inference"
    - "blockdrop dynamic inference paths in residual networks"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "convolutional networks with adaptive inference graphs"
    - "coordinating filters for faster deep neural networks"
    - "residual connections encourage iterative inference"
    - "ultimate tensorization compressing convolutional and fc layers alike"

Connected through hyperedge HE-7 (induced by source paper "word2bits quantized word vectors"):
  with members:
    - "high accuracy low precision training"
    - "fasttext zip compressing text classification models"
    - "compressing word embeddings via deep compositional code learning"
    - "reading wikipedia to answer open domain questions"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "squad 100 000 questions for machine comprehension of text"
    - "bag of tricks for efficient text classification"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-8 (induced by source paper "deep neural decision trees"):
  with members:
    - "challenges for transparency"
    - "xgboost a scalable tree boosting system"
    - "why should i trust you explaining the predictions of any classifier"
    - "hierarchical multiscale recurrent neural networks"
    - "deep learning in neural networks"
    - "efficient non greedy optimization of decision trees"

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)