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: "finding low tension communities"
  Abstract: Motivated by applications that arise in online social media and collaboration networks, there has been a lot of work on community-search and team-formation problems. In the former class of problems, the goal is to find a subgraph that satisfies a certain connectivity requirement and contains a given collection of seed nodes. In the latter class of problems, on the other hand, the goal is to find individuals who collectively have the skills required for a task and form a connected subgraph with certain properties. #R##N#In this paper, we extend both the community-search and the team-formation problems by associating each individual with a profile. The profile is a numeric score that quantifies the position of an individual with respect to a topic. We adopt a model where each individual starts with a latent profile and arrives to a conformed profile through a dynamic conformation process, which takes into account the individual's social interaction and the tendency to conform with one's social environment. In this framework, social tension arises from the differences between the conformed profiles of neighboring individuals as well as from differences between individuals' conformed and latent profiles. #R##N#Given a network of individuals, their latent profiles and this conformation process, we extend the community-search and the team-formation problems by requiring the output subgraphs to have low social tension. From the technical point of view, we study the complexity of these problems and propose algorithms for solving them effectively. Our experimental evaluation in a number of social networks reveals the efficacy and efficiency of our methods.

Connected through hyperedge HE-1 (induced by source paper "a team formation algorithm for faultline minimization"):
  with members:
    - "team formation for scheduling educational material in massive online classes"
    - "towards realistic team formation in social networks based on densest subgraphs"
    - "multi skill collaborative teams based on densest subgraphs"

Connected through hyperedge HE-2 (induced by source paper "maximizing the diversity of exposure in a social network"):
  with members:
    - "minimizing polarization and disagreement in social networks"
    - "balancing information exposure in social networks"
    - "joint non negative matrix factorization for learning ideological leaning on twitter"
    - "revenue maximization in incentivized social advertising"
    - "reducing controversy by connecting opposing views"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "modeling adoption and usage of competing products"

Connected through hyperedge HE-3 (induced by source paper "team formation for scheduling educational material in massive online classes"):
  with members:
    - "towards realistic team formation in social networks based on densest subgraphs"
    - "capacitated team formation problem on social networks"

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)