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: "an automated system for discovering neighborhood patterns in ego networks"
  Abstract: Generally, social network analysis has often focused on the topology of the network without considering the characteristics of individuals involved in them. Less attention is given to study the behavior of individuals, considering they are the basic entity of a graph. Given a mobile social network graph, what are good features to extract key information from the nodes?How many distinct neighborhood patterns exist for ego nodes? What clues does such information provide to study nodes over a long period of time? #R##N#In this report, we develop an automated system in order to discover the occurrences of prototypical ego-centric patterns from data. We aim to provide a data-driven instrument to be used in behavioral sciences for graph interpretations. We analyze social networks derived from real-world data collected with smart-phones. We select 13 well-known network measures, especially those concerned with ego graphs. We form eight feature subsets and then assess their performance using unsupervised clustering techniques to discover distinguishing ego-centric patterns. From clustering analysis, we discover that eight distinct neighborhood patterns have emerged. This categorization allows concise analysis of users' data as they change over time. The results provide a fine-grained analysis for the contribution of different feature sets to detect unique clustering patterns. Last, as a case study, two datasets are studied over long periods to demonstrate the utility of this method. The study shows the effectiveness of the proposed approach in discovering important trends from data.

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)