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: "the role of information asymmetry in the market for university industry research collaboration"
  Abstract: This study concerns the market for research collaboration between industry and universities. It presents an analysis of the population of all Italian university–industry collaborations that resulted in at least one international scientific publication between 2001 and 2003. Using spatial and bibliometric analysis relating to scientific output of university researchers, the study shows the importance of geographic proximity in companies’ choices of university partner. The analysis also reveals inefficiency in the market: in a large proportion of cases private companies could have chosen more qualified research partners in universities located closer to the place of business.

Connected through hyperedge HE-1 (induced by source paper "the north south divide in the italian higher education system"):
  with members:
    - "accounting for gender research performance differences in ranking universities"
    - "do they agree bibliometric evaluation versus informed peer review in the italian research assessment exercise"
    - "a farewell to the mncs and like size independent indicators"
    - "a methodology to measure the effectiveness of academic recruitment and turnover"
    - "the vqr italy s second national research assessment methodological failures and ranking distortions"
    - "should the research performance of scientists be distinguished by gender"
    - "the determinants of academic career advancement evidence from italy"
    - "relatives in the same university faculty nepotism or merit"

Connected through hyperedge HE-2 (induced by source paper "the vqr italy s second national research assessment methodological failures and ranking distortions"):
  with members:
    - "how do you define and measure research productivity"
    - "inefficiency in selecting products for submission to national research assessment exercises"
    - "the importance of accounting for the number of co authors and their order when assessing research performance at the individual level in the life sciences"
    - "national peer review research assessment exercises for the hard sciences can be a complete waste of money the italian case"
    - "revisiting the scaling of citations for research assessment"
    - "a heuristic approach to author name disambiguation in bibliometrics databases for large scale research assessments"
    - "national research assessment exercises a comparison of peer review and bibliometrics rankings"
    - "evaluating research from informed peer review to bibliometrics"

Connected through hyperedge HE-3 (induced by source paper "a bibliometric tool to assess the regional dimension of university industry research collaborations"):
  with members:
    - "a heuristic approach to author name disambiguation in bibliometrics databases for large scale research assessments"
    - "a unified approach to mapping and clustering of bibliometric networks"

Connected through hyperedge HE-4 (induced by source paper "university industry research collaboration a model to assess university capability"):
  with members:
    - "university industry collaboration in italy a bibliometric examination"
    - "the contribution of star scientists to overall sex differences in research productivity"

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.DL(Digital Libraries)