human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "summarizing user generated textual content motivation and methods for fairness in algorithmic summaries"):
  Abstract: As the amount of user-generated textual content grows rapidly, text summarization algorithms are increasingly being used to provide users a quick overview of the information content. Traditionally, summarization algorithms have been evaluated only based on how well they match human-written summaries (e.g. as measured by ROUGE scores). In this work, we propose to evaluate summarization algorithms from a completely new perspective that is important when the user-generated data to be summarized comes from different socially salient user groups, e.g. men or women, Caucasians or African-Americans, or different political groups (Republicans or Democrats). In such cases, we check whether the generated summaries fairly represent these different social groups. Specifically, considering that an extractive summarization algorithm selects a subset of the textual units (e.g. microblogs) in the original data for inclusion in the summary, we investigate whether this selection is fair or not. Our experiments over real-world microblog datasets show that existing summarization algorithms often represent the socially salient user-groups very differently compared to their distributions in the original data. More importantly, some groups are frequently under-represented in the generated summaries, and hence get far less exposure than what they would have obtained in the original data. To reduce such adverse impacts, we propose novel fairness-preserving summarization algorithms which produce high-quality summaries while ensuring fairness among various groups. To our knowledge, this is the first attempt to produce fair text summarization, and is likely to open up an interesting research direction.

Includes members:
  Member "discrimination in the age of algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pretrained ai models performativity mobility and change")
  Member "fair and diverse dpp based data summarization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "interventions for ranking in the presence of implicit bias")
  Member "equity of attention amortizing individual fairness in rankings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fairsight visual analytics for fairness in decision making")
  Member "simplicity creates inequity implications for fairness stereotypes and interpretability":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a resolution in algorithmic fairness calibrated scores for fair classifications")
  Member "fair clustering through fairlets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "algorithmic fairness")
  Member "equality of voice towards fair representation in crowdsourced top k recommendations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the fairness of time critical influence maximization in social networks")
  Member "selection problems in the presence of implicit bias":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fairness in recommendation ranking through pairwise comparisons")
  Member "a survey on neural network based summarization methods":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gasp generating abstracts of scientific papers from abstracts of cited papers")

Question: Please tell me which class the source paper 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)