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: "collaborative filtering with diffusion based similarity on tripartite graphs"
  Abstract: Collaborative tags are playing a more and more important role for the organization of information systems. In this paper, we study a personalized recommendation model making use of the ternary relations among users, objects and tags. We propose a measure of user similarity based on his preference and tagging information. Two kinds of similarities between users are calculated by using a diffusion-based process, which are then integrated for recommendation. We test the proposed method in a standard collaborative filtering framework with three metrics: ranking score, Recall and Precision, and demonstrate that it performs better than the commonly used cosine similarity.

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"
    - "personalized recommendation via integrated diffusion on user item tag 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"
    - "solving the apparent diversity accuracy dilemma of recommender systems"
    - "personalized recommendation via integrated diffusion on user item tag tripartite graphs"

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

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"
    - "personalized recommendation via integrated diffusion on user item tag tripartite graphs"

Connected through hyperedge HE-5 (induced by source paper "information filtering via collaborative user clustering modeling"):
  with members:
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "solving the apparent diversity accuracy dilemma of recommender systems"
    - "link power coordination for energy conservation in complex communication networks"
    - "personalized recommendation via integrated diffusion on user item tag tripartite graphs"

Connected through hyperedge HE-6 (induced by source paper "tag aware recommender systems a state of the art survey"):
  with members:
    - "bpr bayesian personalized ranking from implicit feedback"
    - "social knowledge based recommender system application to the movies domain"
    - "empirical comparison of algorithms for network community detection"
    - "solving the apparent diversity accuracy dilemma of recommender systems"
    - "personalized recommendation via integrated diffusion on user item tag tripartite graphs"
    - "collective dynamics of social annotation"
    - "adaptive model for recommendation of news"
    - "collaborative tagging as a tripartite network"

Connected through hyperedge HE-7 (induced by source paper "effective personalized recommendation in collaborative tagging systems"):
  with members:
    - "personalized recommendation via integrated diffusion on user item tag tripartite graphs"

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)