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 "multi domain collaborative filtering"):
  Abstract: Collaborative filtering is an effective recommendation approach in which the preference of a user on an item is predicted based on the preferences of other users with similar interests. A big challenge in using collaborative filtering methods is the data sparsity problem which often arises because each user typically only rates very few items and hence the rating matrix is extremely sparse. In this paper, we address this problem by considering multiple collaborative filtering tasks in different domains simultaneously and exploiting the relationships between domains. We refer to it as a multi-domain collaborative filtering (MCF) problem. To solve the MCF problem, we propose a probabilistic framework which uses probabilistic matrix factorization to model the rating problem in each domain and allows the knowledge to be adaptively transferred across different domains by automatically learning the correlation between domains. We also introduce the link function for different domains to correct their biases. Experiments conducted on several real-world applications demonstrate the effectiveness of our methods when compared with some representative methods.

Includes members:
  Member "dependency networks for collaborative filtering and data visualization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "probabilistic models for unified collaborative and content based recommendation in sparse data environments")
  Member "empirical analysis of predictive algorithms for collaborative filtering":
    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 "probabilistic models for unified collaborative and content based recommendation in sparse data environments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recommender systems with heterogeneous side information")
  Member "collaborative filtering by personality diagnosis a hybrid memory and model based approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rard ii the 94 million related article recommendation dataset")
  Member "a bayesian approach toward active learning for collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "addressing the item cold start problem by attribute driven active learning")
  Member "ordinal boltzmann machines for collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "joint neural collaborative filtering for recommender systems")
  Member "active collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reinforcement learning for slate based recommender systems a tractable decomposition and practical methodology")
  Member "preference based graphic models for collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "response aware model based collaborative filtering")

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)