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 "fairness preserving text summarzation"):
  Abstract: Given the rapid growth in online information content, text summarization algorithms are progressively used to provide users a succinct idea about the total information content. Historically, summarization algorithms are evaluated only based on how close their outputs are to human-written gold standard summaries. In this work, we propose to evaluate summarization algorithms from an unprecedented viewpoint. Considering that an extractive summarization algorithm selects a subset of the textual units (tweets / sentences) in the input data for inclusion in the summary, we examine the fairness of this selection. Importantly, if the data to be summarized is generated by different socially salient groups, or different political groups, or different news media sources, then we check whether the generated summaries fairly represent these different groups or sources. In real-world datasets we observe that existing summarization algorithms often represent the groups very differently compared to their distributions in the input data. To alleviate such adverse impacts, we propose a novel fair summarization algorithm 'FairSumm' capable of generating high-quality fair summaries.

Includes members:
  Member "fair clustering through fairlets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "algorithmic fairness")
  Member "a survey on neural network based summarization methods":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gasp generating abstracts of scientific papers from abstracts of cited papers")
  Member "trump vs hillary what went viral during the 2016 us presidential election":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "quantifying polarization on twitter the kavanaugh nomination")
  Member "fa ir a fair top k ranking algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fairsight visual analytics for fairness in decision making")
  Member "exposing the probabilistic causal structure of discrimination":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "understanding agent incentives using causal influence diagrams part i single action settings")
  Member "how to be fair and diverse":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "representation learning with statistical independence to mitigate bias")
  Member "summarunner a recurrent neural network based sequence model for extractive summarization of documents":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "prophetnet predicting future n gram for sequence to sequence pre training")
  Member "budgeted influence maximization for multiple products":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distributed submodular maximization")

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)