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: "temporal effects on hashtag reuse in twitter a cognitive inspired hashtag recommendation approach"
  Abstract: Hashtags have become a powerful tool in social platforms such as Twitter to categorize and search for content, and to spread short messages across members of the social network. In this paper, we study temporal hashtag usage practices in Twitter with the aim of designing a cognitive-inspired hashtag recommendation algorithm we call BLLi,s. Our main idea is to incorporate the effect of time on (i) individual hashtag reuse (i.e., reusing own hashtags), and (ii) social hashtag reuse (i.e., reusing hashtags, which has been previously used by a followee) into a predictive model. For this, we turn to the Base-Level Learning (BLL) equation from the cognitive architecture ACT-R, which accounts for the time-dependent decay of item exposure in human memory. We validate BLLi,s using two crawled Twitter datasets in two evaluation scenarios: firstly, only temporal usage patterns of past hashtag assignments are utilized and secondly, these patterns are combined with a content-based analysis of the current tweet. In both scenarios, we find not only that temporal effects play an important role for both individual and social hashtag reuse but also that BLLi,s provides significantly better prediction accuracy and ranking results than current state-of-the-art hashtag recommendation methods.

Connected through hyperedge HE-1 (induced by source paper "should we embed a study on the online performance of utilizing embeddings for real time job recommendations"):
  with members:
    - "neighborhood troubles on the value of user pre filtering to speed up and enhance recommendations"
    - "beyond accuracy optimization on the value of item embeddings for student job recommendations"
    - "pixie a system for recommending 3 billion items to 200 million users in real time"
    - "item2vec neural item embedding for collaborative filtering"
    - "solving cold start problem in large scale recommendation engines a deep learning approach"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (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"
    - "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"
    - "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 "modeling artist preferences of users with different music consumption patterns for fair music recommendations"):
  with members:
    - "the influence of frequency recency and semantic context on the reuse of tags in social tagging systems"

Connected through hyperedge HE-4 (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"
    - "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"
    - "recommending items in social tagging systems using tag and time information"
    - "semantic stability in social tagging streams"

Connected through hyperedge HE-5 (induced by source paper "tweetskb a public and large scale rdf corpus of annotated tweets"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "semantic annotation for microblog topics using wikipedia temporal information"
    - "learning from the news predicting entity popularity on twitter"

Connected through hyperedge HE-6 (induced by source paper "studying confirmation bias in hashtag usage on twitter"):
  with members:
    - "the tagrec framework as a toolkit for the development of tag based recommender systems"
    - "the bursty dynamics of the twitter information network"

Connected through hyperedge HE-7 (induced by source paper "beyond accuracy optimization on the value of item embeddings for student job recommendations"):
  with members:
    - "temporal learning and sequence modeling for a job recommender system"
    - "solving cold start problem in large scale recommendation engines a deep learning approach"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

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)