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: "long time no see the probability of reusing tags as a function of frequency and recency"
  Abstract: In this paper, we introduce a tag recommendation algorithm that mimics the way humans draw on items in their long-term memory. This approach uses the frequency and recency of previous tag assignments to estimate the probability of reusing a particular tag. Using three real-world folksonomies gathered from bookmarks in BibSonomy, CiteULike and Flickr, we show how adding a time-dependent component outperforms conventional "most popular tags" approaches and another existing and very effective but less theory-driven, time-dependent recommendation mechanism. By combining our approach with a simple resource-specific frequency analysis, our algorithm outperforms other well-established algorithms, such as FolkRank, Pairwise Interaction Tensor Factorization and Collaborative Filtering. We conclude that our approach provides an accurate and computationally efficient model of a user's temporal tagging behavior. We show how effective principles for information retrieval can be designed and implemented if human memory processes are taken into account.

Connected through hyperedge HE-1 (induced by source paper "the tagrec framework as a toolkit for the development of tag based recommender systems"):
  with members:
    - "modeling cognitive processes in social tagging to improve tag recommendations"
    - "temporal effects on hashtag reuse in twitter a cognitive inspired hashtag recommendation approach"
    - "the influence of frequency recency and semantic context on the reuse of tags in social tagging systems"
    - "recommending items in social tagging systems using tag and time information"

Connected through hyperedge HE-2 (induced by source paper "modeling activation processes in human memory to improve tag recommendations"):
  with members:
    - "the tagrec framework as a toolkit for the development of tag based recommender systems"
    - "modeling cognitive processes in social tagging to improve tag recommendations"
    - "utilizing online social network and location based data to recommend products and categories in online marketplaces"
    - "forgetting the words but remembering the meaning modeling forgetting in a verbal and semantic tag recommender"
    - "socrecm a scalable social recommender engine for online marketplaces"
    - "recommending items in social tagging systems using tag and time information"

Connected through hyperedge HE-3 (induced by source paper "temporal effects on hashtag reuse in twitter a cognitive inspired hashtag recommendation approach"):
  with members:
    - "the bursty dynamics of the twitter information network"
    - "dynamical classes of collective attention in twitter"

Connected through hyperedge HE-4 (induced by source paper "the influence of frequency recency and semantic context on the reuse of tags in social tagging systems"):
  with members:
    - "semantic stability in social tagging streams"

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)