human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "maximizing the diversity of exposure in a social network"
  Abstract: Social-media platforms have created new ways for citizens to stay informed and participate in public debates. However, to enable a healthy environment for information sharing, social deliberation, and opinion formation, citizens need to be exposed to sufficiently diverse viewpoints that challenge their assumptions, instead of being trapped inside filter bubbles. In this paper, we take a step in this direction and propose a novel approach to maximize the diversity of exposure in a social network. We formulate the problem in the context of information propagation, as a task of recommending a small number of news articles to selected users. We propose a realistic setting where we take into account content and user leanings, and the probability of further sharing an article. This setting allows us to capture the balance between maximizing the spread of information and ensuring the exposure of users to diverse viewpoints. #R##N#The resulting problem can be cast as maximizing a monotone and submodular function subject to a matroid constraint on the allocation of articles to users. It is a challenging generalization of the influence maximization problem. Yet, we are able to devise scalable approximation algorithms by introducing a novel extension to the notion of random reverse-reachable sets. We experimentally demonstrate the efficiency and scalability of our algorithm on several real-world datasets.

Connected through hyperedge HE-1 (induced by source paper "diversifying seeds and audience in social influence maximization"):
  with members:
    - "tell me something my friends do not know diversity maximization in social networks"
    - "balancing information exposure in social networks"
    - "node2vec scalable feature learning for networks"
    - "robust influence maximization"
    - "boosting information spread an algorithmic approach"
    - "line large scale information network embedding"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-2 (induced by source paper "balancing spreads of influence in a social network"):
  with members:
    - "coverage centrality maximization in undirected networks"
    - "tell me something my friends do not know diversity maximization in social networks"
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "balancing information exposure in social networks"
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "the densest k subhypergraph problem"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "maximizing social influence in nearly optimal time"

Connected through hyperedge HE-3 (induced by source paper "understanding filter bubbles and polarization in social networks"):
  with members:
    - "opinion dynamics with varying susceptibility to persuasion"
    - "eigenvector computation and community detection in asynchronous gossip models"
    - "how algorithmic confounding in recommendation systems increases homogeneity and decreases utility"
    - "find your place simple distributed algorithms for community detection"
    - "opinion maximization in social networks"
    - "biased assimilation homophily and the dynamics of polarization"
    - "how bad is forming your own opinion"

Question: Please tell me which class the center node 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)