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: "conditional restricted boltzmann machines for structured output prediction"
  Abstract: Conditional Restricted Boltzmann Machines (CRBMs) are rich probabilistic models that have recently been applied to a wide range of problems, including collaborative filtering, classification, and modeling motion capture data. While much progress has been made in training non-conditional RBMs, these algorithms are not applicable to conditional models and there has been almost no work on training and generating predictions from conditional RBMs for structured output problems. We first argue that standard Contrastive Divergence-based learning may not be suitable for training CRBMs. We then identify two distinct types of structured output prediction problems and propose an improved learning algorithm for each. The first problem type is one where the output space has arbitrary structure but the set of likely output configurations is relatively small, such as in multi-label classification. The second problem is one where the output space is arbitrarily structured but where the output space variability is much greater, such as in image denoising or pixel labeling. We show that the new learning algorithms can work much better than Contrastive Divergence on both types of problems.

Connected through hyperedge HE-1 (induced by source paper "towards social artificial intelligence nonverbal social signal prediction in a triadic interaction"):
  with members:
    - "on the convergence of adam and beyond"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "learning to generate long term future via hierarchical prediction"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "recent trends in deep learning based natural language processing"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "hand keypoint detection in single images using multiview bootstrapping"

Connected through hyperedge HE-2 (induced by source paper "probabilistic discriminative learning with layered graphical models"):
  with members:
    - "belief propagation in conditional rbms for structured prediction"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "the mythos of model interpretability"
    - "bidirectional lstm crf models for sequence tagging"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "loopy belief propagation for approximate inference an empirical study"

Connected through hyperedge HE-3 (induced by source paper "machine learning for comprehensive forecasting of alzheimer s disease progression"):
  with members:
    - "scalable and accurate deep learning for electronic health records"

Connected through hyperedge HE-4 (induced by source paper "restricted boltzmann machines with gaussian visible units guided by pairwise constraints"):
  with members:
    - "gaussian binary restricted boltzmann machines for modeling natural image statistics"
    - "estimating 3d trajectories from 2d projections via disjunctive factored four way conditional restricted boltzmann machines"
    - "theta rbm unfactored gated restricted boltzmann machine for rotation invariant representations"
    - "a novel feature extraction method for scene recognition based on centered convolutional restricted boltzmann machines"
    - "deep transductive semi supervised maximum margin clustering"
    - "a generative restricted boltzmann machine based method for high dimensional motion data modeling"
    - "speech recognition with deep recurrent neural networks"
    - "modeling documents with deep boltzmann machines"

Connected through hyperedge HE-5 (induced by source paper "energy based models for video anomaly detection"):
  with members:
    - "learning temporal regularity in video sequences"
    - "importance weighted autoencoders"
    - "a recurrent latent variable model for sequential data"
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "conditional generative adversarial nets"
    - "learning to generate chairs with convolutional neural networks"
    - "fast gradient based inference with continuous latent variable models in auxiliary form"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

Connected through hyperedge HE-6 (induced by source paper "belief propagation in conditional rbms for structured prediction"):
  with members:
    - "decomposition bounds for marginal map"
    - "loopy belief propagation for approximate inference an empirical study"
    - "efficient inference in fully connected crfs with gaussian edge potentials"
    - "autotagging music with conditional restricted boltzmann machines"

Connected through hyperedge HE-7 (induced by source paper "discriminative conditional restricted boltzmann machine for discrete choice and latent variable modelling"):
  with members:
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-8 (induced by source paper "building energy load forecasting using deep neural networks"):
  with members:
    - "electricity demand forecasting by multi task learning"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"
    - "dropout improves recurrent neural networks for handwriting recognition"
    - "on the difficulty of training recurrent neural networks"

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)