human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "large scale product categorization using structured and unstructured attributes"):
  Abstract: Product categorization using text data for eCommerce is a very challenging extreme classification problem with several thousands of classes and several millions of products to classify. Even though multi-class text classification is a well studied problem both in academia and industry, most approaches either deal with treating product content as a single pile of text, or only consider a few product attributes for modelling purposes. Given the variety of products sold on popular eCommerce platforms, it is hard to consider all available product attributes as part of the modeling exercise, considering that products possess their own unique set of attributes based on category. In this paper, we compare hierarchical models to flat models and show that in specific cases, flat models perform better. We explore two Deep Learning based models that extract features from individual pieces of unstructured data from each product and then combine them to create a product signature. We also propose a novel idea of using structured attributes and their values together in an unstructured fashion along with convolutional filters such that the ordering of the attributes and the differing attributes by product categories no longer becomes a modelling challenge. This approach is also more robust to the presence of faulty product attribute names and values and can elegantly generalize to use both closed list and open list attributes.

Includes members:
  Member "don t classify translate multi level e commerce product categorization via machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "atlas a dataset and benchmark for e commerce clothing product categorization")
  Member "entity embeddings of categorical variables":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fastai a layered api for deep learning")
  Member "product classification in e commerce using distributional semantics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "open world learning and application to product classification")
  Member "is a picture worth a thousand words a deep multi modal fusion architecture for product classification in e commerce":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep neural linear bandits overcoming catastrophic forgetting through likelihood matching")
  Member "cyclical learning rates for training neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "towards universal paraphrastic sentence embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improving candidate generation for low resource cross lingual entity linking")
  Member "convolutional neural networks for sentence classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

Question: Please tell me which class the source paper 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.IR(Information Retrieval)