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: "accelerating the pace of discovery by changing the peer review algorithm"
  Abstract: The number of scientific publications is constantly rising, increasing the strain on the review process. The number of submissions is actually higher, as each manuscript is often reviewed several times before publication. To face the deluge of submissions, top journals reject a considerable fraction of manuscripts without review, potentially declining manuscripts with merit. The situation is frustrating for authors, reviewers and editors alike. Recently, several editors wrote about the ``tragedy of the reviewer commons', advocating for urgent corrections to the system. Almost every scientist has ideas on how to improve the system, but it is very difficult, if not impossible, to perform experiments to test which measures would be most effective. Surprisingly, relatively few attempts have been made to model peer review. Here I implement a simulation framework in which ideas on peer review can be quantitatively tested. I incorporate authors, reviewers, manuscripts and journals into an agent-based model and a peer review system emerges from their interactions. As a proof-of-concept, I contrast an implementation of the current system, in which authors decide the journal for their submissions, with a system in which journals bid on manuscripts for publication. I show that, all other things being equal, this latter system solves most of the problems currently associated with the peer review process. Manuscripts' evaluation is faster, authors publish more and in better journals, and reviewers' effort is optimally utilized. However, more work is required from editors. This modeling framework can be used to test other solutions for peer review, leading the way for an improvement of how science is disseminated.

Connected through hyperedge HE-1 (induced by source paper "how to create an innovation accelerator"):
  with members:
    - "from social data mining to forecasting socio economic crises"
    - "from social simulation to integrative system design"
    - "citing and reading behaviours in high energy physics how a community stopped worrying about journals and learned to love repositories"
    - "visual conceptualizations and models of science"
    - "maps on the basis of the arts humanities citation index the journals leonardo and art journal versus digital humanities as a topic"

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)