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: "topic based influence computation in social networks under resource constraints"
  Abstract: As social networks are constantly changing and evolving, methods to analyze dynamic social networks are becoming more important in understanding social trends. However, due to the restrictions imposed by the social network service providers, the resources available to fetch the entire contents of a social network are typically very limited. As a result, analysis of dynamic social network data requires maintaining an approximate copy of the social network for each time period, locally. In this paper, we study the problem of dynamic network and text fetching with limited probing capacities, for identifying and maintaining influential users as the social network evolves. We propose an algorithm to probe the relationships (required for global influence computation) as well as posts (required for topic-based influence computation) of a limited number of users during each probing period, based on the influence trends and activities of the users. We infer the current network based on the newly probed user data and the last known version of the network maintained locally. Additionally, we propose to use link prediction methods to further increase the accuracy of our network inference. We employ PageRank as the metric for influence computation. We illustrate how the proposed solution maintains accurate PageRank scores for computing global influence, and topic-sensitive weighted PageRank scores for topic-based influence. The latter relies on a topic-based network constructed via weights determined by semantic analysis of posts and their sharing statistics. We evaluate the effectiveness of our algorithms by comparing them with the true influence scores of the full and up-to-date version of the network, using data from the micro-blogging service Twitter. Results show that our techniques significantly outperform baseline methods (80% higher accuracy for network fetching and 77% for text fetching) and are superior to state-of-the-art techniques from the literature (21% higher accuracy).

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)