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: "building a location based set of social media users"
  Abstract: In many instances one may want to gain situational awareness in an environment by monitoring the content of local social media users. Often the challenge is how to build a set of users from a target location. Here we introduce a method for building such a set of users by using an \emph{expand-classify} approach which begins with a small set of seed users from the target location and then iteratively collects their neighbors and then classifies their locations. We perform this classification using maximum likelihood estimation on a factor graph model which incorporates features of the user profile and also social network connections. We show that maximum likelihood estimation reduces to solving a minimum cut problem on an appropriately defined graph. We are able to obtain several thousand users within a few hours for many diverse locations using our approach. Using geo-located data, we find that our approach typically achieves good accuracy for population centers with less than 500,000 inhabitants, while for larger cities performance degrades somewhat. We also find that our approach is able to collect many more users with higher accuracy than existing search methods. Finally, we show that by studying the content of location specific users obtained with our approach, we can identify the onset of significant social unrest in locations such as the Philippines.

Connected through hyperedge HE-1 (induced by source paper "detecting influence campaigns in social networks using the ising model"):
  with members:
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"

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)