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: "a probabilistic model to resolve diversity accuracy challenge of recommendation systems"
  Abstract: Recommendation systems have wide-spread applications in both academia and industry. Traditionally, performance of recommendation systems has been measured by their precision. By introducing novelty and diversity as key qualities in recommender systems, recently increasing attention has been focused on this topic. Precision and novelty of recommendation are not in the same direction, and practical systems should make a trade-off between these two quantities. Thus, it is an important feature of a recommender system to make it possible to adjust diversity and accuracy of the recommendations by tuning the model. In this paper, we introduce a probabilistic structure to resolve the diversity---accuracy dilemma in recommender systems. We propose a hybrid model with adjustable level of diversity and precision such that one can perform this by tuning a single parameter. The proposed recommendation model consists of two models: one for maximization of the accuracy and the other one for specification of the recommendation list to tastes of users. Our experiments on two real datasets show the functionality of the model in resolving accuracy---diversity dilemma and outperformance of the model over other classic models. The proposed method could be extensively applied to real commercial systems due to its low computational complexity and significant performance.

Connected through hyperedge HE-1 (induced by source paper "link prediction in multiplex networks based on interlayer similarity"):
  with members:
    - "qanet tensor decomposition approach for query based anomaly detection in heterogeneous information networks"
    - "a multilayer approach to multiplexity and link prediction in online geo social networks"
    - "spreading processes in multilayer networks"
    - "multiple routes transmitted epidemics on multiplex networks"
    - "diffusion of innovations over multiplex social networks"
    - "combinatorial analysis of multiple networks"

Connected through hyperedge HE-2 (induced by source paper "the graph based broad behavior aware recommendation system for interactive news"):
  with members:
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "very deep convolutional networks for end to end speech recognition"
    - "zero resource machine translation by multimodal encoder decoder network with multimedia pivot"
    - "node2vec scalable feature learning for networks"
    - "deep residual learning for image recognition"
    - "deepwalk online learning of social representations"
    - "very deep convolutional networks for large scale image recognition"
    - "collaborative deep learning for recommender systems"

Connected through hyperedge HE-3 (induced by source paper "how algorithmic confounding in recommendation systems increases homogeneity and decreases utility"):
  with members:
    - "recommendations as treatments debiasing learning and evaluation"
    - "estimating the causal impact of recommendation systems from observational data"
    - "modeling mutual feedback between users and recommender systems"
    - "reducing offline evaluation bias in recommendation systems"
    - "discrimination in online ad delivery"
    - "biased assimilation homophily and the dynamics of polarization"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

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)