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: "personalized recommendation via integrated diffusion on user item tag tripartite graphs"
  Abstract: Personalized recommender systems are confronting great challenges of accuracy, diversification and novelty, especially when the data set is sparse and lacks accessorial information, such as user profiles, item attributes and explicit ratings. Collaborative tags contain rich information about personalized preferences and item contents, and are therefore potential to help in providing better recommendations. In this article, we propose a recommendation algorithm based on an integrated diffusion on user–item–tag tripartite graphs. We use three benchmark data sets, Del.icio.us, MovieLens and BibSonomy, to evaluate our algorithm. Experimental results demonstrate that the usage of tag information can significantly improve accuracy, diversification and novelty of recommendations.

Connected through hyperedge HE-1 (induced by source paper "recommending investors for new startups by integrating network diffusion and investors domain preference"):
  with members:
    - "inferring users preferences through leveraging their social relationships"
    - "network based recommendation algorithms a review"
    - "metadata embeddings for user and item cold start recommendations"
    - "recommending investors for crowdfunding projects"
    - "collaborative filtering with diffusion based similarity on tripartite graphs"

Connected through hyperedge HE-2 (induced by source paper "coarse cluster enhancing collaborative recommendation for social network systems"):
  with members:
    - "ultra accurate collaborative information filtering via directed user similarity"
    - "personalized recommendation with corrected similarity"
    - "information filtering via collaborative user clustering modeling"
    - "uncovering the information core in recommender systems"
    - "tag aware recommender systems a state of the art survey"
    - "collaborative filtering with diffusion based similarity on tripartite graphs"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

Connected through hyperedge HE-3 (induced by source paper "graph based collaborative ranking"):
  with members:
    - "bpr bayesian personalized ranking from implicit feedback"
    - "collaborative filtering with diffusion based similarity on tripartite graphs"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

Connected through hyperedge HE-4 (induced by source paper "leveraging tagging and rating for recommendation rmf meets weighted diffusion on tripartite graphs"):
  with members:
    - "tag aware recommender systems a state of the art survey"
    - "collaborative filtering with diffusion based similarity on tripartite graphs"

Connected through hyperedge HE-5 (induced by source paper "diffusion like recommendation with enhanced similarity of objects"):
  with members:
    - "information filtering on coupled social networks"
    - "information filtering via balanced diffusion on bipartite networks"
    - "tag aware recommender systems a state of the art survey"
    - "solving the apparent diversity accuracy dilemma of recommender systems"
    - "adaptive model for recommendation of news"

Connected through hyperedge HE-6 (induced by source paper "sibrank signed bipartite network analysis for neighbor based collaborative ranking"):
  with members:
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-7 (induced by source paper "a probabilistic model to resolve diversity accuracy challenge of recommendation systems"):
  with members:
    - "personalized news recommendation with context trees"
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "using temporal data for making recommendations"
    - "an mdp based recommender system"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

Connected through hyperedge HE-8 (induced by source paper "information filtering on coupled social networks"):
  with members:
    - "information filtering by similarity preferential diffusion processes"
    - "information filtering in sparse online systems recommendation via semi local diffusion"
    - "membership in social networks and the application in information filtering"
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "promotional effect on cold start problem and diversity in a data characteristic based recommendation method"
    - "tag aware recommender systems a state of the art survey"
    - "solving the apparent diversity accuracy dilemma of recommender systems"
    - "the rich club phenomenon in the internet topology"

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)