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 "group fairness in influence maximization"):
  Abstract: Influence maximization is a widely used model for information dissemination in social networks. Recent work has employed such interventions across a wide range of social problems, spanning public health, substance abuse, and international development (to name a few examples). A critical but understudied question is whether the benefits of such interventions are fairly distributed across different groups in the population; e.g., avoiding discrimination with respect to sensitive attributes such as race or gender. Drawing on legal and game-theoretic concepts, we introduce formal definitions of fairness in influence maximization. We provide an algorithmic framework to find solutions which satisfy fairness constraints, and in the process improve the state of the art for general multi-objective submodular maximization problems. Experimental results on real data from an HIV prevention intervention for homeless youth show that standard influence maximization techniques oftentimes neglect smaller groups which contribute less to overall utility, resulting in a disparity which our proposed algorithms substantially reduce.

Includes members:
  Member "fair allocation of indivisible public goods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "participatory budgeting models and approaches")
  Member "groupwise maximin fair allocation of indivisible goods":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "maximin fairness with mixed divisible and indivisible goods")
  Member "multi objective maximization of monotone submodular functions with cardinality constraint":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient algorithms for generating provably near optimal cluster descriptors for explainability")
  Member "diverse weighted bipartite b matching":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "forming diverse teams from sequentially arriving people")
  Member "approximate maximin shares for groups of agents":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "algorithmic fairness")
  Member "stochastic submodular maximization the case of coverage functions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "end to end learning and optimization on graphs")
  Member "gradient methods for submodular maximization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "black box submodular maximization discrete and continuous settings")

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.GT(Computer Science and Game Theory)