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 "balancing spreads of influence in a social network"):
  Abstract: The personalization of our news consumption on social media has a tendency to reinforce our pre-existing beliefs instead of balancing our opinions. This finding is a concern for the health of our democracies which rely on an access to information providing diverse viewpoints. To tackle this issue from a computational perspective, Garimella et al. (NIPS'17) modeled the spread of these viewpoints, also called campaigns, using the well-known independent cascade model and studied an optimization problem that aims at balancing information exposure in a social network when two opposing campaigns propagate in the network. The objective in their $NP$-hard optimization problem is to maximize the number of people that are exposed to either both or none of the viewpoints. For two different settings, one corresponding to a model where campaigns spread in a correlated manner, and a second one, where the two campaigns spread in a heterogeneous manner, they provide constant ratio approximation algorithms. In this paper, we investigate a more general formulation of this problem. That is, we assume that $\mu$ different campaigns propagate in a social network and we aim to maximize the number of people that are exposed to either $\nu$ or none of the campaigns, where $\mu\ge\nu\ge2$. We provide dedicated approximation algorithms for both the correlated and heterogeneous settings. Interestingly, for the heterogeneous setting with $\nu\ge 3$, we give a reduction leading to several approximation hardness results. Maybe most importantly, we obtain that the problem cannot be approximated within a factor of $n^{-g(n)}$ for any $g(n)=o(1)$ assuming Gap-ETH, denoting with $n$ the number of nodes in the social network. For $\nu \ge 4$, there is no $n^{-\epsilon}$-approximation algorithm if a certain class of one-way functions exists, where $\epsilon > 0$ is a given constant which depends on $\nu$.

Includes members:
  Member "coverage centrality maximization in undirected networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the fixed parameter tractability of the maximum connectivity improvement problem")
  Member "tell me something my friends do not know diversity maximization in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "diversifying seeds and audience in social influence maximization")
  Member "political discourse on social media echo chambers gatekeepers and the price of bipartisanship":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "facebook ads monitor an independent auditing system for political ads on facebook")
  Member "maximizing the diversity of exposure in a social network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "diversifying seeds and audience in social influence maximization")
  Member "balancing information exposure in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "combating fake news a survey on identification and mitigation techniques")
  Member "almost polynomial ratio eth hardness of approximating densest k subgraph":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimizing item and subgroup configurations for social aware vr shopping")
  Member "the densest k subhypergraph problem":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "iterated two phase local search for the set union knapsack problem")
  Member "from competition to complementarity comparative influence diffusion and maximization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on adaptivity gaps of influence maximization under the independent cascade model with full adoption feedback")

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)