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: "utilizing online social network and location based data to recommend products and categories in online marketplaces"
  Abstract: Recent research has unveiled the importance of online social networks for improving the quality of recommender systems and encouraged the research community to investigate better ways of exploiting the social information for recommendations. To contribute to this sparse field of research, in this paper we exploit users' interactions along three data sources (marketplace, social network and location-based) to assess their performance in a barely studied domain: recommending products and domains of interests (i.e., product categories) to people in an online marketplace environment. To that end we defined sets of content- and network-based user similarity features for each data source and studied them isolated using an user-based Collaborative Filtering (CF) approach and in combination via a hybrid recommender algorithm, to assess which one provides the best recommendation performance. Interestingly, in our experiments conducted on a rich dataset collected from SecondLife, a popular online virtual world, we found that recommenders relying on user similarity features obtained from the social network data clearly yielded the best results in terms of accuracy in case of predicting products, whereas the features obtained from the marketplace and location-based data sources also obtained very good results in case of predicting categories. This finding indicates that all three types of data sources are important and should be taken into account depending on the level of specialization of the recommendation task.

Connected through hyperedge HE-1 (induced by source paper "overcoming the imbalance between tag recommendation approaches and real world folksonomy structures with cognitive inspired algorithms"):
  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"
    - "temporal effects on hashtag reuse in twitter a cognitive inspired hashtag recommendation approach"
    - "forgetting the words but remembering the meaning modeling forgetting in a verbal and semantic tag recommender"
    - "recommending items in social tagging systems using tag and time information"
    - "semantic stability in social tagging streams"

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"
    - "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"
    - "long time no see the probability of reusing tags as a function of frequency and recency"

Connected through hyperedge HE-3 (induced by source paper "trust based collaborative filtering tackling the cold start problem using regular equivalence"):
  with members:
    - "beyond accuracy optimization on the value of item embeddings for student job recommendations"
    - "node2vec scalable feature learning for networks"

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)