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: "a theory of local learning the learning channel and the optimality of backpropagation"
  Abstract: In a physical neural system, where storage and processing are intimately intertwined, the rules for adjusting the synaptic weights can only depend on variables that are available locally, such as the activity of the pre- and post-synaptic neurons, resulting in local learning rules. A systematic framework for studying the space of local learning rules is obtained by first specifying the nature of the local variables, and then the functional form that ties them together into each learning rule. Such a framework enables also the systematic discovery of new learning rules and exploration of relationships between learning rules and group symmetries. We study polynomial local learning rules stratified by their degree and analyze their behavior and capabilities in both linear and non-linear units and networks. Stacking local learning rules in deep feedforward networks leads to deep local learning. While deep local learning can learn interesting representations, it cannot learn complex input-output functions, even when targets are available for the top layer. Learning complex input-output functions requires local deep learning where target information is communicated to the deep layers through a backward learning channel. The nature of the communicated information about the targets and the structure of the learning channel partition the space of learning algorithms. For any learning algorithm, the capacity of the learning channel can be defined as the number of bits provided about the error gradient per weight, divided by the number of required operations per weight. We estimate the capacity associated with several learning algorithms and show that backpropagation outperforms them by simultaneously maximizing the information rate and minimizing the computational cost. This result is also shown to be true for recurrent networks, by unfolding them in time. The theory clarifies the concept of Hebbian learning, establishes the power and limitations of local learning rules, introduces the learning channel which enables a formal analysis of the optimality of backpropagation, and explains the sparsity of the space of learning rules discovered so far.

Connected through hyperedge HE-1 (induced by source paper "pr product a substitute for inner product in neural networks"):
  with members:
    - "neural baby talk"
    - "context aware visual policy network for sequence level image captioning"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "recurrent fusion network for image captioning"
    - "auto encoding scene graphs for image captioning"
    - "image generation from scene graphs"
    - "exploring visual relationship for image captioning"
    - "improved image captioning via policy gradient optimization of spider"

Connected through hyperedge HE-2 (induced by source paper "questions to guide the future of artificial intelligence research"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "one pixel attack for fooling deep neural networks"
    - "learning in the machine to share or not to share"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "relational recurrent neural networks"
    - "deep learning a critical appraisal"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "hindsight experience replay"

Connected through hyperedge HE-3 (induced by source paper "asymmetric residual neural network for accurate human activity recognition"):
  with members:
    - "deep attention guided hashing"
    - "dfternet towards 2 bit dynamic fusion networks for accurate human activity recognition"
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "compression artifacts removal using convolutional neural networks"
    - "spatiotemporal residual networks for video action recognition"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-4 (induced by source paper "spikegrad an ann equivalent computation model for implementing backpropagation with spikes"):
  with members:
    - "deep spiking neural network with spike count based learning rule"
    - "a spiking network for inference of relations trained with neuromorphic backpropagation"
    - "deep learning in spiking neural networks"
    - "training deep neural networks for binary communication with the whetstone method"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "direct training for spiking neural networks faster larger better"

Connected through hyperedge HE-5 (induced by source paper "surrogate gradient learning in spiking neural networks"):
  with members:
    - "biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets"
    - "deep learning in spiking neural networks"
    - "deep networks incorporating spiking neural dynamics"
    - "synaptic plasticity dynamics for deep continuous local learning"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "slayer spike layer error reassignment in time"
    - "smplr deep smpl reverse for 3d human pose and shape recovery"
    - "temporally efficient deep learning with spikes"

Connected through hyperedge HE-6 (induced by source paper "direct feedback alignment with sparse connections for local learning"):
  with members:
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "understanding reuse performance and hardware cost of dnn dataflows a data centric approach"
    - "deep supervised learning using local errors"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "learning in the machine random backpropagation and the deep learning channel"
    - "tensorflow a system for large scale machine learning"
    - "very deep convolutional networks for large scale image recognition"
    - "difference target propagation"

Connected through hyperedge HE-7 (induced by source paper "the capacity of feedforward neural networks"):
  with members:
    - "nearly tight vc dimension and pseudodimension bounds for piecewise linear neural networks"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "understanding deep learning requires rethinking generalization"
    - "the power of depth for feedforward neural networks"
    - "deep learning in neural networks"

Connected through hyperedge HE-8 (induced by source paper "gradient target propagation"):
  with members:
    - "biologically motivated algorithms for propagating local target representations"
    - "speeding up the hyperparameter optimization of deep convolutional neural networks"
    - "on weight initialization in deep neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "hyperparameters optimization in deep convolutional neural network bayesian approach with gaussian process prior"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "how auto encoders could provide credit assignment in deep networks via target propagation"
    - "adam a method for stochastic optimization"

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)