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 misleading narrative of the canonical faculty productivity trajectory"
  Abstract: A scientist may publish tens or hundreds of papers over a career, but these contributions are not evenly spaced in time. Sixty years of studies on career productivity patterns in a variety of fields suggest an intuitive and universal pattern: Productivity tends to rise rapidly to an early peak and then gradually declines. Here, we test the universality of this conventional narrative by analyzing the structures of individual faculty productivity time series, constructed from over 200,000 publications and matched with hiring data for 2,453 tenure-track faculty in all 205 PhD-granting computer science departments in the United States and Canada. Unlike prior studies, which considered only some faculty or some institutions, or lacked common career reference points, here we combine a large bibliographic dataset with comprehensive information on career transitions that covers an entire field of study. We show that the conventional narrative confidently describes only one-fifth of faculty, regardless of department prestige or researcher gender, and the remaining four-fifths of faculty exhibit a rich diversity of productivity patterns. To explain this diversity, we introduce a simple model of productivity trajectories and explore correlations between its parameters and researcher covariates, showing that departmental prestige predicts overall individual productivity and the timing of the transition from first- to last-author publications. These results demonstrate the unpredictability of productivity over time and open the door for new efforts to understand how environmental and individual factors shape scientific productivity.

Connected through hyperedge HE-1 (induced by source paper "discovering archetypes to interpret evolution of individual behavior"):
  with members:
    - "can we count on social media metrics first insights into the active scholarly use of social media"
    - "career transitions and trajectories a case study in computing"
    - "gender productivity and prestige in computer science faculty hiring networks"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"
    - "clustering hidden markov models with variational hem"
    - "analysis of computer science communities based on dblp"

Connected through hyperedge HE-2 (induced by source paper "historical comparison of gender inequality in scientific careers across countries and disciplines"):
  with members:
    - "gender productivity and prestige in computer science faculty hiring networks"
    - "the journal coverage of web of science and scopus a comparative analysis"
    - "quantifying long term scientific impact"
    - "gender differences in research productivity a bibliometric analysis of the italian academic system"
    - "the contribution of star scientists to overall sex differences in research productivity"

Connected through hyperedge HE-3 (induced by source paper "career transitions and trajectories a case study in computing"):
  with members:
    - "representing higher order dependencies in networks"
    - "network structure inference a survey motivations methods and applications"
    - "pnp fast path ensemble method for movie design"
    - "events and controversies influences of a shocking news event on information seeking"
    - "pagerank beyond the web"

Connected through hyperedge HE-4 (induced by source paper "prestige drives epistemic inequality in the diffusion of scientific ideas"):
  with members:
    - "effectiveness of anonymization in double blind review"
    - "tracing the use of practices through networks of collaboration"
    - "gender productivity and prestige in computer science faculty hiring networks"
    - "can cascades be predicted"

Connected through hyperedge HE-5 (induced by source paper "automatically assembling a full census of an academic field"):
  with members:
    - "gender productivity and prestige in computer science faculty hiring networks"

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)