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: "predicting links in ego networks using temporal information"
  Abstract: Link prediction appears as a central problem of network science, as it calls for unfolding the mechanisms that govern the micro-dynamics of the network. In this work, we are interested in ego-networks, that is the mere information of interactions of a node to its neighbors, in the context of social relationships. As the structural information is very poor, we rely on another source of information to predict links among egos' neighbors: the timing of interactions. We define several features to capture different kinds of temporal information and apply machine learning methods to combine these various features and improve the quality of the prediction. We demonstrate the efficiency of this temporal approach on a cellphone interaction dataset, pointing out features which prove themselves to perform well in this context, in particular the temporal profile of interactions and elapsed time between contacts.

Connected through hyperedge HE-1 (induced by source paper "community discovery in dynamic networks a survey"):
  with members:
    - "stream graphs and link streams for the modeling of interactions over time"
    - "revealing evolutions in dynamical networks"
    - "the ground truth about metadata and community detection in networks"
    - "on the evaluation potential of quality functions in community detection for different contexts"
    - "a framework for the construction of generative models for mesoscale structure in multilayer networks"
    - "generative benchmark models for mesoscale structures in multilayer networks"
    - "enumerating maximal cliques in temporal graphs"
    - "using multiple criteria methods to evaluate community partitions"

Connected through hyperedge HE-2 (induced by source paper "senator we sell ads analysis of the 2016 russian facebook ads campaign"):
  with members:
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "measuring social spam and the effect of bots on information diffusion in social media"
    - "advances in pre training distributed word representations"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "early detection of promoted campaigns on social media"
    - "online human bot interactions detection estimation and characterization"
    - "botornot a system to evaluate social bots"
    - "the rise of social bots"

Connected through hyperedge HE-3 (induced by source paper "predicting interactions between individuals with structural and dynamical information"):
  with members:
    - "stream graphs and link streams for the modeling of interactions over time"
    - "link prediction in social networks the state of the art"
    - "rankmerging a supervised learning to rank framework to predict links in large social network"
    - "identifying roles in an ip network with temporal and structural density"
    - "time varying graphs and dynamic networks"

Connected through hyperedge HE-4 (induced by source paper "ndlib a python library to model and analyze diffusion processes over complex networks"):
  with members:
    - "community discovery in dynamic networks a survey"
    - "snap a general purpose network analysis and graph mining library"
    - "computing maximal cliques in link streams"
    - "time varying graphs and dynamic networks"

Connected through hyperedge HE-5 (induced by source paper "personal vs know how contacts which matter more in wiki elections"):
  with members:
    - "wikipedia traffic data and electoral prediction towards theoretically informed models"
    - "140 characters to victory using twitter to predict the uk 2015 general election"

Connected through hyperedge HE-6 (induced by source paper "the role of gender in social network organization"):
  with members:
    - "using deep learning to predict demographics from mobile phone metadata"
    - "measuring large scale social networks with high resolution"

Connected through hyperedge HE-7 (induced by source paper "parsimonious data how a single facebook like predicts voting behavior in multiparty systems"):
  with members:
    - "wikipedia traffic data and electoral prediction towards theoretically informed models"

Connected through hyperedge HE-8 (induced by source paper "divergent discourse between protests and counter protests blacklivesmatter and alllivesmatter"):
  with members:
    - "is language evolution grinding to a halt exploring the life and death of words in english fiction"
    - "characterizing the demographics behind the blacklivesmatter movement"
    - "content and network dynamics behind egyptian political polarization on twitter"
    - "the digital evolution of occupy wall street"
    - "the geospatial characteristics of a social movement communication network"
    - "partisan asymmetries in online political activity"

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)