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 "joint optimization of tree based index and deep model for recommender systems"):
  Abstract: Large-scale industrial recommender systems are usually confronted with computational problems due to the enormous corpus size. To retrieve and recommend the most relevant items to users under response time limits, resorting to an efficient index structure is an effective and practical solution. Tree-based Deep Model (TDM) for recommendation \cite{zhu2018learning} greatly improves recommendation accuracy using tree index. By indexing items in a tree hierarchy and training a user-node preference prediction model satisfying a max-heap like property in the tree, TDM provides logarithmic computational complexity w.r.t. the corpus size, enabling the use of arbitrary advanced models in candidate retrieval and recommendation. #R##N#In tree-based recommendation methods, the quality of both the tree index and the trained user preference prediction model determines the recommendation accuracy for the most part. We argue that the learning of tree index and user preference model has interdependence. Our purpose, in this paper, is to develop a method to jointly learn the index structure and user preference prediction model. In our proposed joint optimization framework, the learning of index and user preference prediction model are carried out under a unified performance measure. Besides, we come up with a novel hierarchical user preference representation utilizing the tree index hierarchy. Experimental evaluations with two large-scale real-world datasets show that the proposed method improves recommendation accuracy significantly. Online A/B test results at Taobao display advertising also demonstrate the effectiveness of the proposed method in production environments.

Includes members:
  Member "practice on long sequential user behavior modeling for click through rate prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mixed dimension embeddings with application to memory efficient recommendation systems")
  Member "deep learning based recommender system a survey and new perspectives":
    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 "xdeepfm combining explicit and implicit feature interactions for recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cross gcn enhancing graph convolutional network with k order feature interactions")
  Member "the case for learned index structures":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "monotonic cardinality estimation of similarity selection a deep learning approach")
  Member "neural collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "advances in collaborative filtering and ranking")
  Member "billion scale similarity search with gpus":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "optimized cost per click in taobao display advertising":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "motiac multi objective actor critics for real time bidding")
  Member "wide deep learning for recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)