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 "conceptualize and infer user needs in e commerce"):
  Abstract: Understanding latent user needs beneath shopping behaviors is critical to e-commercial applications. Without a proper definition of user needs in e-commerce, most industry solutions are not driven directly by user needs at current stage, which prevents them from further improving user satisfaction. Representing implicit user needs explicitly as nodes like "outdoor barbecue" or "keep warm for kids" in a knowledge graph, provides new imagination for various e- commerce applications. Backed by such an e-commerce knowledge graph, we propose a supervised learning algorithm to conceptualize user needs from their transaction history as "concept" nodes in the graph and infer those concepts for each user through a deep attentive model. Offline experiments demonstrate the effectiveness and stability of our model, and online industry strength tests show substantial advantages of such user needs understanding.

Includes members:
  Member "deep session interest network for click through rate prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sdm sequential deep matching model for online large scale recommender system")
  Member "learning heterogeneous knowledge base embeddings for explainable recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on knowledge graph based recommender systems")
  Member "ripplenet propagating user preferences on the knowledge graph for recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on knowledge graph based recommender systems")
  Member "explainable reasoning over knowledge graphs for recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on knowledge graph based recommender systems")
  Member "explainable recommendation a survey and new perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on knowledge graph based recommender systems")
  Member "deep cascade multi task learning for slot filling in online shopping assistant":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dialogact2vec towards end to end dialogue agent by multi task representation learning")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "neural rating regression with abstractive tips generation for recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "developing multi task recommendations with long term rewards via policy distilled reinforcement learning")

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)