human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "making neural networks fair"):
  Abstract: Research on neural networks has gained significant momentum over the past few years. A plethora of neural networks is currently being trained on available data in research as well as in industry. Because training is a resource-intensive process and training data cannot always be made available to everyone, there has been a recent trend to attempt to re-use already-trained neural networks. As such, neural networks themselves have become research data. In this paper, we present the Neural Network Ontology, an ontology to make neural networks findable, accessible, interoperable and reusable as suggested by the well-established FAIR guiding principles for scientific data management and stewardship. We created the new FAIRnets Dataset that comprises about 2,000 neural networks openly accessible on the internet and uses the Neural Network Ontology to semantically annotate and represent the neural networks. For each of the neural networks in the FAIRnets Dataset, the relevant properties according to the Neural Network Ontology such as the description and the architecture are stored. Ultimately, the FAIRnets Dataset can be queried with a set of desired properties and responds with a set of neural networks that have these properties. We provide the service FAIRnets Search which is implemented on top of a SPARQL endpoint and allows for querying, searching and finding trained neural networks annotated with the Neural Network Ontology. The service is demonstrated by a browser-based frontend to the SPARQL endpoint.

Includes members:
  Member "model cards for model reporting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "explainability fact sheets a framework for systematic assessment of explainable approaches")
  Member "deep visual semantic alignments for generating image descriptions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")

Question: Please tell me which class the source paper 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.LG(Machine Learning)