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: "behavior revealed in mobile phone usage predicts loan repayment"
  Abstract: Many households in developing countries lack formal financial histories, making it difficult for banks to extend loans, and for potential borrowers to receive them. However, many of these households have mobile phones, which generate rich data about behavior. This paper shows that behavioral signatures in mobile phone data predict loan default. We evaluate our approach using call records matched to lending outcomes in a middle income South American country. Individuals in the highest quartile of risk by our measure are 7.4 times more likely to default than those in the lowest quartile. The method is predictive for both individuals with financial histories, and those without, who cannot be scored using traditional methods. We benchmark performance on our sample of individuals with (thin) financial histories: our method performs no worse than models using credit bureau information. The method can form the basis for new forms of lending that reach the unbanked.

Connected through hyperedge HE-1 (induced by source paper "e t rnn applying deep learning to credit loan applications"):
  with members:
    - "lisa explaining recurrent neural network judgments via layer wise semantic accumulation and example to pattern transformation"
    - "rnns implicitly implement tensor product representations"
    - "snapshot ensembles train 1 get m for free"
    - "sgdr stochastic gradient descent with restarts"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "cyclical learning rates for training neural networks"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-2 (induced by source paper "predicting complex user behavior from cdr based social networks"):
  with members:
    - "community detection using a neighborhood strength driven label propagation algorithm"
    - "slpa uncovering overlapping communities in social networks via a speaker listener interaction dynamic process"

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.CY(Computers and Society)