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: "product classification in e commerce using distributional semantics"
  Abstract: Product classification is the task of automatically predicting a taxonomy path for a product in a predefined taxonomy hierarchy given a textual product description or title. For efficient product classification we require a suitable representation for a document (the textual description of a product) feature vector and efficient and fast algorithms for prediction. To address the above challenges, we propose a new distributional semantics representation for document vector formation. We also develop a new two-level ensemble approach utilizing (with respect to the taxonomy tree) a path-wise, node-wise and depth-wise classifiers for error reduction in the final product classification. Our experiments show the effectiveness of the distributional representation and the ensemble approach on data sets from a leading e-commerce platform and achieve better results on various evaluation metrics compared to earlier approaches.

Connected through hyperedge HE-1 (induced by source paper "open world learning and application to product classification"):
  with members:
    - "lifelong domain word embedding via meta learning"
    - "efficient large scale domain classification with personalized attention"
    - "learning to teach"
    - "probabilistic model agnostic meta learning"
    - "doc deep open classification of text documents"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "pathnet evolution channels gradient descent in super neural networks"

Connected through hyperedge HE-2 (induced by source paper "improving document classification with multi sense embeddings"):
  with members:
    - "topic modeling in embedding spaces"
    - "the dynamic embedded topic model"
    - "improving topic models with latent feature word representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "concatenated p mean word embeddings as universal cross lingual sentence representations"
    - "probabilistic fasttext for multi sense word embeddings"
    - "efficient vector representation for documents through corruption"

Connected through hyperedge HE-3 (induced by source paper "large scale product categorization using structured and unstructured attributes"):
  with members:
    - "don t classify translate multi level e commerce product categorization via machine translation"
    - "entity embeddings of categorical variables"
    - "is a picture worth a thousand words a deep multi modal fusion architecture for product classification in e commerce"
    - "cyclical learning rates for training neural networks"
    - "towards universal paraphrastic sentence embeddings"
    - "convolutional neural networks for sentence classification"

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.AI(Artificial Intelligence)