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: "profiling presence patterns and segmenting user locations from cell phone data"
  Abstract: The dynamic monitoring of commuting flows is crucial for improving transit systems in fast-developing cities around the world. However, existing methodology to infer commuting originations and destinations have to either rely on large-scale survey data, which is inherently expensive to implement, or on Call Detail Records but based on ad-hoc heuristic assignment rules based on the frequency of appearance at given locations. In this paper, we proposed a novel method to accurately infer the point of origin and destinations of commuting flows based on individual's spatial-temporal patterns inferred from Call Detail Records. Our project significantly improves the accuracies upon the heuristic assignment rules popularly adopted in the literature. Starting with the historical data of geo-temporal travel pattern for a panel of individuals, we create, for each person-location, a vector of probability distribution capturing the likelihood that the person will appear in that location for a given the time of day. Stacked in this way, the matrix of historical geo-temporal data enables us to apply Eigen-decomposition and use unsupervised machine learning techniques to extract commonalities across locations for the different group of travelers, which ultimately allows us to make inferences and create labels, such as home and work, on specific locations. Testing the methodology on real-world data with known location labels shows that our method identifies home and workplaces with significant accuracy, improving upon the most commonly used methods in the literature by 79\% and 34\%, respectively. Most importantly, our methodology does not bear any significant computation burden and is easily scalable and easily expanded to other real-world data with historical tracking.

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)