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: "a study of age and gender seen through mobile phone usage patterns in mexico"
  Abstract: Mobile phone usage provides a wealth of information, which can be used to better understand the demographic structure of a population. In this paper we focus on the population of Mexican mobile phone users. Our first contribution is an observational study of mobile phone usage according to gender and age groups. We were able to detect significant differences in phone usage among different subgroups of the population. Our second contribution is to provide a novel methodology to predict demographic features (namely age and gender) of unlabeled users by leveraging individual calling patterns, as well as the structure of the communication graph. We provide details of the methodology and show experimental results on a real world dataset that involves millions of users.

Connected through hyperedge HE-1 (induced by source paper "snel sql native execution for llvm"):
  with members:
    - "the value of big data for credit scoring enhancing financial inclusion using mobile phone data and social network analytics"
    - "uncovering the spread of chagas disease in argentina and mexico"
    - "comparison of feature extraction methods and predictors for income inference"
    - "inference of users demographic attributes based on homophily in communication networks"
    - "social events in a time varying mobile phone graph"
    - "social network analytics for churn prediction in telco"
    - "the city pulse of buenos aires"
    - "prepaid or postpaid that is the question novel methods of subscription type prediction in mobile phone services"

Connected through hyperedge HE-2 (induced by source paper "link centric analysis of variation by demographics in mobile phone communication patterns"):
  with members:
    - "predicting customer s gender and age depending on mobile phone data"
    - "different patterns of social closeness observed in mobile phone communication"
    - "uncovering intimate and casual relationships from mobile phone communication"
    - "modeling the temporal nature of human behavior for demographics prediction"
    - "inference of demographic attributes based on mobile phone usage patterns and social network topology"
    - "computational sociolinguistics a survey"

Connected through hyperedge HE-3 (induced by source paper "predicting customer s gender and age depending on mobile phone data"):
  with members:
    - "identifying seasonal mobility profiles from anonymized and aggregated mobile phone data application in food security"
    - "xgboost a scalable tree boosting system"
    - "modeling the temporal nature of human behavior for demographics prediction"

Connected through hyperedge HE-4 (induced by source paper "the value of big data for credit scoring enhancing financial inclusion using mobile phone data and social network analytics"):
  with members:
    - "social network analytics for churn prediction in telco"
    - "socioeconomic correlations and stratification in social communication networks"

Connected through hyperedge HE-5 (induced by source paper "cognitive inference of demographic data by user ratings"):
  with members:
    - "unsupervised visual representation learning by context prediction"
    - "harnessing mobile phone social network topology to infer users demographic attributes"
    - "distributed representations of words and phrases and their compositionality"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-6 (induced by source paper "a bayesian approach to income inference in a communication network"):
  with members:
    - "the city pulse of buenos aires"
    - "harnessing mobile phone social network topology to infer users demographic attributes"
    - "the anatomy of the facebook social graph"

Connected through hyperedge HE-7 (induced by source paper "you are what apps you use demographic prediction based on user s apps"):
  with members:
    - "harnessing mobile phone social network topology to infer users demographic attributes"

Connected through hyperedge HE-8 (induced by source paper "using deep learning to predict demographics from mobile phone metadata"):
  with members:
    - "teaching machines to read and comprehend"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "bayesopt a bayesian optimization library for nonlinear optimization experimental design and bandits"
    - "caffe convolutional architecture for fast feature embedding"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "representation learning a review and new perspectives"

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)