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 recomputation manifesto"
  Abstract: Replication of scientific experiments is critical to the advance of science. Unfortunately, the discipline of Computer Science has never treated replication seriously, even though computers are very good at doing the same thing over and over again. Not only are experiments rarely replicated, they are rarely even replicable in a meaningful way. Scientists are being encouraged to make their source code available, but this is only a small step. Even in the happy event that source code can be built and run successfully, running code is a long way away from being able to replicate the experiment that code was used for. I propose that the discipline of Computer Science must embrace replication of experiments as standard practice. I propose that the only credible technique to make experiments truly replicable is to provide copies of virtual machines in which the experiments are validated to run. I propose that tools and repositories should be made available to make this happen. I propose to be one of those who makes it happen.

Connected through hyperedge HE-1 (induced by source paper "frustrated with replicating claims of a shared model a solution"):
  with members:
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "lstm benchmarks for deep learning frameworks"
    - "latency and throughput characterization of convolutional neural networks for mobile computer vision"
    - "characterizing co located datacenter workloads an alibaba case study"
    - "tbd benchmarking and analyzing deep neural network training"
    - "tensorflow serving flexible high performance ml serving"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dlpaper2code auto generation of code from deep learning research papers"

Connected through hyperedge HE-2 (induced by source paper "reproducibility as a technical specification"):
  with members:
    - "towards reproducibility as a service"
    - "top tips to make your research irreproducible"
    - "community driven reviewing and validation of publications"
    - "can i implement your algorithm a model for reproducible research software"
    - "share and enjoy publishing useful and usable scientific models"
    - "best practices for scientific computing"
    - "case studies and challenges in reproducibility in the computational sciences"

Connected through hyperedge HE-3 (induced by source paper "top tips to make your research irreproducible"):
  with members:
    - "can i implement your algorithm a model for reproducible research software"
    - "share and enjoy publishing useful and usable scientific models"
    - "best practices for scientific computing"

Connected through hyperedge HE-4 (induced by source paper "can i implement your algorithm a model for reproducible research software"):
  with members:
    - "community driven reviewing and validation of publications"
    - "software carpentry lessons learned"

Connected through hyperedge HE-5 (induced by source paper "share and enjoy publishing useful and usable scientific models"):
  with members:
    - "community driven reviewing and validation of publications"
    - "can i implement your algorithm a model for reproducible research software"

Connected through hyperedge HE-6 (induced by source paper "case studies and challenges in reproducibility in the computational sciences"):
  with members:
    - "the shape of the search tree for the maximum clique problem and the implications for parallel branch and bound"

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.GL(General Literature)