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: "early inference in energy based models approximates back propagation"
  Abstract: We show that Langevin MCMC inference in an energy-based model with latent variables has the property that the early steps of inference, starting from a stationary point, correspond to propagating error gradients into internal layers, similarly to back-propagation. The error that is back-propagated is with respect to visible units that have received an outside driving force pushing them away from the stationary point. Back-propagated error gradients correspond to temporal derivatives of the activation of hidden units. This observation could be an element of a theory for explaining how brains perform credit assignment in deep hierarchies as efficiently as back-propagation does. In this theory, the continuous-valued latent variables correspond to averaged voltage potential (across time, spikes, and possibly neurons in the same minicolumn), and neural computation corresponds to approximate inference and error back-propagation at the same time.

Connected through hyperedge HE-1 (induced by source paper "deep neural networks abstract like humans"):
  with members:
    - "on the implicit bias of dropout"
    - "exploring generalization in deep learning"
    - "visualizing the loss landscape of neural nets"
    - "the loss surface of deep and wide neural networks"
    - "opening the black box of deep neural networks via information"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "a downsampled variant of imagenet as an alternative to the cifar datasets"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-2 (induced by source paper "shallow unorganized neural networks using smart neuron model for visual perception"):
  with members:
    - "deep learning a critical appraisal"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "wavenet a generative model for raw audio"
    - "a neural algorithm of artistic style"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "assessing the scalability of biologically motivated deep learning algorithms and architectures"):
  with members:
    - "biologically motivated algorithms for propagating local target representations"
    - "conducting credit assignment by aligning local representations"
    - "feedforward initialization for fast inference of deep generative networks is biologically plausible"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"
    - "towards biologically plausible deep learning"
    - "how auto encoders could provide credit assignment in deep networks via target propagation"
    - "adam a method for stochastic optimization"
    - "difference target propagation"

Connected through hyperedge HE-4 (induced by source paper "deep supervised learning using local errors"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "hardware efficient on line learning through pipelined truncated error backpropagation in binary state networks"
    - "understanding synthetic gradients and decoupled neural interfaces"
    - "vlsi implementation of deep neural network using integral stochastic computing"
    - "nullhop a flexible convolutional neural network accelerator based on sparse representations of feature maps"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "densely connected convolutional networks"
    - "decoupled neural interfaces using synthetic gradients"

Connected through hyperedge HE-5 (induced by source paper "equilibrium propagation bridging the gap between energy based models and backpropagation"):
  with members:
    - "unbiased online recurrent optimization"
    - "towards deep learning with spiking neurons in energy based models with contrastive hebbian plasticity"
    - "feedforward initialization for fast inference of deep generative networks is biologically plausible"
    - "stdp as presynaptic activity times rate of change of postsynaptic activity"
    - "why are deep nets reversible a simple theory with implications for training"
    - "training recurrent networks online without backtracking"
    - "towards biologically plausible deep learning"
    - "an objective function for stdp"

Connected through hyperedge HE-6 (induced by source paper "towards a biologically plausible backprop"):
  with members:
    - "stdp as presynaptic activity times rate of change of postsynaptic activity"
    - "why are deep nets reversible a simple theory with implications for training"
    - "training recurrent networks online without backtracking"
    - "towards biologically plausible deep learning"

Connected through hyperedge HE-7 (induced by source paper "feedforward initialization for fast inference of deep generative networks is biologically plausible"):
  with members:
    - "towards a biologically plausible backprop"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"
    - "difference target propagation"

Connected through hyperedge HE-8 (induced by source paper "an objective function for stdp"):
  with members:
    - "towards a biologically plausible backprop"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"
    - "towards biologically plausible deep learning"

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)