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: "non negative sparse coding"
  Abstract: Non-negative sparse coding is a method for decomposing multivariate data into non-negative sparse components. In this paper we briefly describe the motivation behind this type of data representation and its relation to standard sparse coding and non-negative matrix factorization. We then give a simple yet efficient multiplicative algorithm for finding the optimal values of the hidden components. In addition, we show how the basis vectors can be learned from the observed data. Simulations demonstrate the effectiveness of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "distributed bayesian matrix decomposition for big data mining and clustering"):
  with members:
    - "bayesian joint matrix decomposition for data integration with heterogeneous noise"
    - "mllib machine learning in apache spark"
    - "a survey on multi view learning"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "scalable k means"

Connected through hyperedge HE-2 (induced by source paper "word embedding visualization via dictionary learning"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "towards understanding linear word analogies"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "spine sparse interpretable neural embeddings"
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-3 (induced by source paper "unsupervised algorithm for disaggregating low sampling rate electricity consumption of households"):
  with members:
    - "review of smart meter data analytics applications methodologies and challenges"
    - "non intrusive energy disaggregation using non negative matrix factorization with sum to k constraint"
    - "a survey on non intrusive load monitoring methodies and techniques for energy disaggregation problem"
    - "neural nilm deep neural networks applied to energy disaggregation"
    - "the uk dale dataset domestic appliance level electricity demand and whole house demand from five uk homes"
    - "gspbox a toolbox for signal processing on graphs"

Connected through hyperedge HE-4 (induced by source paper "exact and consistent interpretation for piecewise linear neural networks a closed form solution"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "smoothgrad removing noise by adding noise"
    - "interpreting deep visual representations via network dissection"
    - "deep embedding forest forest based serving with deep embedding features"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"
    - "nearly tight vc dimension and pseudodimension bounds for piecewise linear neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-5 (induced by source paper "using sparse semantic embeddings learned from multimodal text and image data to model human conceptual knowledge"):
  with members:
    - "semantic structure and interpretability of word embeddings"
    - "are distributional representations ready for the real world evaluating word vectors for grounded perceptual meaning"
    - "vqa visual question answering"
    - "sparse overcomplete word vector representations"
    - "combining language and vision with a multimodal skip gram model"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "visualizing and understanding convolutional networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "learning the hierarchical parts of objects by deep non smooth nonnegative matrix factorization"):
  with members:
    - "sparse deep nonnegative matrix factorization"
    - "efficient nonnegative tucker decompositions algorithms and uniqueness"
    - "a deep matrix factorization method for learning attribute representations"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-7 (induced by source paper "an integrated inverse space sparse representation framework for tumor classification"):
  with members:
    - "regularized robust coding for face recognition"
    - "gene selection with guided regularized random forest"

Connected through hyperedge HE-8 (induced by source paper "breast tumor classification based on decision information genes and inverse projection sparse representation"):
  with members:
    - "regularized robust coding for face recognition"
    - "gene selection with guided regularized random forest"

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.NE(Neural and Evolutionary Computing)