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: "tag aware recommender systems a state of the art survey"
  Abstract: In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related studies and future challenges of tag-aware recommendation algorithms.

Connected through hyperedge HE-1 (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"
    - "collaborative filtering with diffusion based similarity on tripartite graphs"
    - "solving the apparent diversity accuracy dilemma of recommender systems"
    - "personalized recommendation via integrated diffusion on user item tag tripartite graphs"

Connected through hyperedge HE-2 (induced by source paper "leveraging tagging and rating for recommendation rmf meets weighted diffusion on tripartite graphs"):
  with members:
    - "collaborative filtering with diffusion based similarity on tripartite graphs"
    - "personalized recommendation via integrated diffusion on user item tag tripartite graphs"

Connected through hyperedge HE-3 (induced by source paper "network based recommendation algorithms a review"):
  with members:
    - "a vertex similarity index for better personalized recommendation"
    - "modeling mutual feedback between users and recommender systems"
    - "information filtering by similarity preferential diffusion processes"
    - "information filtering in sparse online systems recommendation via semi local diffusion"
    - "network based information filtering algorithms ranking and recommendation"
    - "trend prediction in temporal bipartite networks the case of movielens netflix and digg"
    - "solving the apparent diversity accuracy dilemma of recommender systems"
    - "improved collaborative filtering algorithm via information transformation"

Connected through hyperedge HE-4 (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"
    - "solving the apparent diversity accuracy dilemma of recommender systems"
    - "personalized recommendation via integrated diffusion on user item tag tripartite graphs"
    - "adaptive model for recommendation of news"

Connected through hyperedge HE-5 (induced by source paper "parallel and distributed collaborative filtering a survey"):
  with members:
    - "factorbird a parameter server approach to distributed matrix factorization"
    - "gapfm optimal top n recommendations for graded relevance domains"
    - "mli an api for distributed machine learning"
    - "distributed user profiling via spectral methods"
    - "efficient multicore collaborative filtering"
    - "a survey paper on recommender systems"
    - "solving k nearest neighbor problem on multiple graphics processors"
    - "the impact of social networks on multi agent recommender systems"

Connected through hyperedge HE-6 (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"
    - "solving the apparent diversity accuracy dilemma of recommender systems"
    - "personalized recommendation via integrated diffusion on user item tag tripartite graphs"
    - "the rich club phenomenon in the internet topology"

Connected through hyperedge HE-7 (induced by source paper "information filtering via balanced diffusion on bipartite networks"):
  with members:
    - "information filtering on coupled social networks"
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

Connected through hyperedge HE-8 (induced by source paper "conquering the rating bound problem in neighborhood based collaborative filtering a function recovery approach"):
  with members:
    - "solving the apparent diversity accuracy dilemma of recommender systems"
    - "exact matrix completion via convex optimization"

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)