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: "online semi supervised learning with deep hybrid boltzmann machines and denoising autoencoders"
  Abstract: Two novel deep hybrid architectures, the Deep Hybrid Boltzmann Machine and the Deep Hybrid Denoising Auto-encoder, are proposed for handling semi-supervised learning problems. The models combine experts that model relevant distributions at different levels of abstraction to improve overall predictive performance on discriminative tasks. Theoretical motivations and algorithms for joint learning for each are presented. We apply the new models to the domain of data-streams in work towards life-long learning. The proposed architectures show improved performance compared to a pseudo-labeled, drop-out rectifier network.

Connected through hyperedge HE-1 (induced by source paper "spiking neural predictive coding for continual learning from data streams"):
  with members:
    - "a biologically plausible supervised learning method for spiking neural networks using the symmetric stdp rule"
    - "unsupervised learning by competing hidden units"
    - "lifelong neural predictive coding sparsity yields less forgetting when learning cumulatively"
    - "bio inspired digit recognition using reward modulated spike timing dependent plasticity in deep convolutional networks"
    - "like a baby visually situated neural language acquisition"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "continual learning of recurrent neural networks by locally aligning distributed representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "continual learning of recurrent neural networks by locally aligning distributed representations"):
  with members:
    - "a neural temporal model for human motion prediction"
    - "sparse attentive backtracking long range credit assignment in recurrent networks"
    - "approximating real time recurrent learning with random kronecker factors"
    - "biologically motivated algorithms for propagating local target representations"
    - "conducting credit assignment by aligning local representations"
    - "lstm a search space odyssey"
    - "learning to adapt by minimizing discrepancy"
    - "learning simpler language models with the differential state framework"

Connected through hyperedge HE-3 (induced by source paper "conducting credit assignment by aligning local representations"):
  with members:
    - "learning to adapt by minimizing discrepancy"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "decoupled neural interfaces using synthetic gradients"
    - "piecewise latent variables for neural variational text processing"
    - "a modular theory of feature learning"
    - "all you need is a good init"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "biologically motivated algorithms for propagating local target representations"):
  with members:
    - "multi layered gradient boosting decision trees"
    - "conducting credit assignment by aligning local representations"
    - "learning to adapt by minimizing discrepancy"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "decoupled neural interfaces using synthetic gradients"
    - "towards biologically plausible deep learning"
    - "how important is weight symmetry in backpropagation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "adaptive representation selection in contextual bandit with unlabeled history"):
  with members:
    - "a neural networks committee for the contextual bandit problem"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-6 (induced by source paper "learning to adapt by minimizing discrepancy"):
  with members:
    - "learning simpler language models with the differential state framework"
    - "unbiased online recurrent optimization"
    - "training language models using target propagation"
    - "exploiting spatio temporal structure with recurrent winner take all networks"
    - "decoupled neural interfaces using synthetic gradients"
    - "piecewise latent variables for neural variational text processing"
    - "learning in the machine random backpropagation and the deep learning channel"
    - "deep predictive coding networks for video prediction and unsupervised learning"

Connected through hyperedge HE-7 (induced by source paper "piecewise latent variables for neural variational text processing"):
  with members:
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "improving variational inference with inverse autoregressive flow"
    - "multiresolution recurrent neural networks an application to dialogue response generation"
    - "smart reply automated response suggestion for email"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "generative deep neural networks for dialogue a short review"
    - "variational lossy autoencoder"
    - "document neural autoregressive distribution estimation"

Connected through hyperedge HE-8 (induced by source paper "privacy protection for natural language records neural generative models for releasing synthetic twitter data"):
  with members:
    - "a persona based neural conversation model"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "a neural network approach to context sensitive generation of conversational responses"
    - "gated feedback recurrent neural networks"
    - "a neural conversational model"
    - "distributed representations of sentences and documents"
    - "generating sequences with recurrent neural networks"
    - "calibrating data to sensitivity in private data analysis"

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)