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: "greedy deep dictionary learning"
  Abstract: In this work we propose a new deep learning tool called deep dictionary learning. Multi-level dictionaries are learnt in a greedy fashion, one layer at a time. This requires solving a simple (shallow) dictionary learning problem, the solution to this is well known. We apply the proposed technique on some benchmark deep learning datasets. We compare our results with other deep learning tools like stacked autoencoder and deep belief network; and state of the art supervised dictionary learning tools like discriminative KSVD and label consistent KSVD. Our method yields better results than all.

Connected through hyperedge HE-1 (induced by source paper "hp vpinns variational physics informed neural networks with domain decomposition"):
  with members:
    - "understanding and mitigating gradient pathologies in physics informed neural networks"
    - "variational physics informed neural networks for solving partial differential equations"
    - "nonlinear approximation and deep relu networks"
    - "varnet variational neural networks for the solution of partial differential equations"
    - "function approximation by deep networks"
    - "d3m a deep domain decomposition method for partial differential equations"
    - "locally adaptive activation functions with slope recovery term for deep and physics informed neural networks"
    - "deepxde a deep learning library for solving differential equations"

Connected through hyperedge HE-2 (induced by source paper "variational physics informed neural networks for solving partial differential equations"):
  with members:
    - "nonlinear approximation and deep relu networks"
    - "nonlinear approximation via compositions"
    - "function approximation by deep networks"
    - "locally adaptive activation functions with slope recovery term for deep and physics informed neural networks"
    - "barron spaces and the compositional function spaces for neural network models"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "hidden fluid mechanics a navier stokes informed deep learning framework for assimilating flow visualization data"
    - "machine learning of linear differential equations using gaussian processes"

Connected through hyperedge HE-3 (induced by source paper "deep micro dictionary learning and coding network"):
  with members:
    - "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"
    - "convolutional dictionary learning acceleration and convergence"
    - "structure aware classification using supervised dictionary learning"
    - "disentangling factors of variation in deep representations using adversarial training"
    - "discriminative bayesian dictionary learning for classification"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "convolutional clustering for unsupervised learning"
    - "pcanet a simple deep learning baseline for image classification"

Connected through hyperedge HE-4 (induced by source paper "magnifyme aiding cross resolution face recognition via identity aware synthesis"):
  with members:
    - "pixel recursive super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "studying very low resolution recognition using deep networks"
    - "image super resolution using deep convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "context aware pedestrian motion prediction in urban intersections"):
  with members:
    - "predictive positioning and quality of service ridesharing for campus mobility on demand systems"
    - "dynamic arrival rate estimation for campus mobility on demand network graphs"

Connected through hyperedge HE-6 (induced by source paper "sparse factorization layers for neural networks with limited supervision"):
  with members:
    - "learning representations for automatic colorization"
    - "youtube 8m a large scale video classification benchmark"
    - "densecap fully convolutional localization networks for dense captioning"
    - "deep residual learning for image recognition"
    - "action conditional video prediction using deep networks in atari games"
    - "anticipating visual representations from unlabeled video"
    - "very deep convolutional networks for large scale image recognition"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-7 (induced by source paper "depth estimation from single image using sparse representations"):
  with members:
    - "deep convolutional neural fields for depth estimation from a single image"
    - "depth extraction from video using non parametric sampling"

Connected through hyperedge HE-8 (induced by source paper "how to train your deep neural network with dictionary learning"):
  with members:
    - "more algorithms for provable dictionary 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)