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 "on the fairness of time critical influence maximization in social networks"):
  Abstract: Influence maximization has found applications in a wide range of real-world problems, for instance, viral marketing of products in an online social network, and information propagation of valuable information such as job vacancy advertisements and health-related information. While existing algorithmic techniques usually aim at maximizing the total number of people influenced, the population often comprises several socially salient groups, e.g., based on gender or race. As a result, these techniques could lead to disparity across different groups in receiving important information. Furthermore, in many of these applications, the spread of influence is time-critical, i.e., it is only beneficial to be influenced before a time deadline. As we show in this paper, the time-criticality of the information could further exacerbate the disparity of influence across groups. This disparity, introduced by algorithms aimed at maximizing total influence, could have far-reaching consequences, impacting people's prosperity and putting minority groups at a big disadvantage. In this work, we propose a notion of group fairness in time-critical influence maximization. We introduce surrogate objective functions to solve the influence maximization problem under fairness considerations. By exploiting the submodularity structure of our objectives, we provide computationally efficient algorithms with guarantees that are effective in enforcing fairness during the propagation process. We demonstrate the effectiveness of our approach through synthetic and real-world experiments.

Includes members:
  Member "group fairness in influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ai for social impact learning and planning in the data to deployment pipeline")
  Member "learning optimal and fair decision trees for non discriminative decision making":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "decision trees for decision making under the predict then optimize framework")
  Member "gaps in information access in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fairness across network positions in cyberbullying detection algorithms")
  Member "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning")
  Member "equality of voice towards fair representation in crowdsourced top k recommendations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "summarizing user generated textual content motivation and methods for fairness in algorithmic summaries")
  Member "fair allocation of indivisible public goods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "participatory budgeting models and approaches")
  Member "approximate maximin shares for groups of agents":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "maximin fairness with mixed divisible and indivisible goods")
  Member "multiwinner elections with diversity constraints":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "algorithmic fairness")

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.SI(Social and Information Networks)