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: "collaborative company profiling insights from an employee s perspective"
  Abstract: Company profiling is an analytical process to build an indepth understanding of company's fundamental characteristics. It serves as an effective way to gain vital information of the target company and acquire business intelligence. Traditional approaches for company profiling rely heavily on the availability of rich finance information about the company, such as finance reports and SEC filings, which may not be readily available for many private companies. However, the rapid prevalence of online employment services enables a new paradigm - to obtain the variety of company's information from their employees' online ratings and comments. This, in turn, raises the challenge to develop company profiles from an employee's perspective. To this end, in this paper, we propose a method named Company Profiling based Collaborative Topic Regression (CPCTR), for learning the latent structural patterns of companies. By formulating a joint optimization framework, CPCTR has the ability in collaboratively modeling both textual (e.g., reviews) and numerical information (e.g., salaries and ratings). Indeed, with the identified patterns, including the positive/negative opinions and the latent variable that influences salary, we can effectively carry out opinion analysis and salary prediction. Extensive experiments were conducted on a real-world data set to validate the effectiveness of CPCTR. The results show that our method provides a comprehensive understanding of company characteristics and delivers a more effective prediction of salaries than other baselines.

Connected through hyperedge HE-1 (induced by source paper "person job fit adapting the right talent for the right job with joint representation learning"):
  with members:
    - "enhancing person job fit for talent recruitment an ability aware neural network approach"
    - "election with bribed voter uncertainty hardness and approximation algorithm"
    - "measuring the popularity of job skills in recruitment market a multi criteria approach"
    - "comparative study of cnn and rnn for natural language processing"
    - "recruitment market trend analysis with sequential latent variable models"
    - "combining recurrent and convolutional neural networks for relation classification"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "enhancing person job fit for talent recruitment an ability aware neural network approach"):
  with members:
    - "measuring the popularity of job skills in recruitment market a multi criteria approach"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "recruitment market trend analysis with sequential latent variable models"
    - "teaching machines to read and comprehend"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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.IR(Information Retrieval)