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 "specializing joint representations for the task of product recommendation"):
  Abstract: We propose a unified product embedded representation that is optimized for the task of retrieval-based product recommendation. To this end, we introduce a new way to fuse modality-specific product embeddings into a joint product embedding, in order to leverage both product content information, such as textual descriptions and images, and product collaborative filtering signal. By introducing the fusion step at the very end of our architecture, we are able to train each modality separately, allowing us to keep a modular architecture that is preferable in real-world recommendation deployments. We analyze our performance on normal and hard recommendation setups such as cold-start and cross-category recommendations and achieve good performance on a large product shopping dataset.

Includes members:
  Member "field aware factorization machines in a real world online advertising system":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "metaselector meta learning for recommendation with user level adaptive model selection")
  Member "wide deep learning for recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "meta prod2vec product embeddings using side information for recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "swivel improving embeddings by noticing what s missing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "word embedding algorithms as generalized low rank models and their canonical form")
  Member "node2vec scalable feature learning for networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep graph matching consensus")
  Member "quantized neural networks training neural networks with low precision weights and activations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "switchable precision neural networks")
  Member "learning visual clothing style with heterogeneous dyadic co occurrences":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "regularizing reasons for outfit evaluation with gradient penalty")
  Member "vbpr visual bayesian personalized ranking from implicit feedback":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "recommender systems based on generative adversarial networks a problem driven perspective")

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)