human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "active collaborative filtering"
  Abstract: Collaborative filtering (CF) allows the preferences of multiple users to be pooled to make recommendations regarding unseen products. We consider in this paper the problem of online and interactive CF: given the current ratings associated with a user, what queries (new ratings) would most improve the quality of the recommendations made? We cast this terms of expected value of information (EVOI); but the online computational cost of computing optimal queries is prohibitive. We show how offline prototyping and computation of bounds on EVOI can be used to dramatically reduce the required online computation. The framework we develop is general, but we focus on derivations and empirical study in the specific case of the multiple-cause vector quantization model.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning for slate based recommender systems a tractable decomposition and practical methodology"):
  with members:
    - "dopamine a research framework for deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "learning a deep listwise context model for ranking refinement"
    - "horizon facebook s open source applied reinforcement learning platform"
    - "discrete sequential prediction of continuous actions for deep rl"
    - "reinforcement learning based recommender system using biclustering technique"
    - "top k off policy correction for a reinforce recommender system"
    - "seq2slate re ranking and slate optimization with rnns"

Connected through hyperedge HE-2 (induced by source paper "embedded collaborative filtering for cold start prediction"):
  with members:
    - "item2vec neural item embedding for collaborative filtering"

Connected through hyperedge HE-3 (induced by source paper "from social network to semantic social network in recommender system"):
  with members:
    - "semantic social network analysis"

Connected through hyperedge HE-4 (induced by source paper "multi domain collaborative filtering"):
  with members:
    - "dependency networks for collaborative filtering and data visualization"
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "probabilistic models for unified collaborative and content based recommendation in sparse data environments"
    - "collaborative filtering by personality diagnosis a hybrid memory and model based approach"
    - "a bayesian approach toward active learning for collaborative filtering"
    - "ordinal boltzmann machines for collaborative filtering"
    - "preference based graphic models for collaborative filtering"

Connected through hyperedge HE-5 (induced by source paper "a bayesian approach toward active learning for collaborative filtering"):
  with members:
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "collaborative filtering by personality diagnosis a hybrid memory and model based approach"
    - "collaborative ensemble learning combining collaborative and content based information filtering via hierarchical bayes"

Question: Please tell me which class the center node 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)