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 "where are we using scopus to map the literature at the intersection between artificial intelligence and crime"):
  Abstract: Research on Artificial Intelligence (AI) applications has spread over many scientific disciplines. Scientists have tested the power of intelligent algorithms developed to predict (or learn from) natural, physical and social phenomena. This also applies to crime-related research problems. Nonetheless, studies that map the current state of the art at the intersection between AI and crime are lacking. What are the current research trends in terms of topics in this area? What is the structure of scientific collaboration when considering works investigating criminal issues using machine learning, deep learning and AI in general? What are the most active countries in this specific scientific sphere? Using data retrieved from Scopus database, this work quantitatively analyzes published works at the intersection between AI and crime employing network science to respond to these questions. Results show that researchers are mainly focusing on cyber-related criminal topics and that relevant themes such as algorithmic discrimination, fairness, and ethics are considerably overlooked. Furthermore, data highlight the extremely disconnected structure of co-authorship networks. Such disconnectedness may represent a substantial obstacle to a more solid community of scientists interested in these topics. Additionally, the graph of scientific collaboration indicates that countries that are more prone to engage in international partnerships are generally less central in the network. This means that scholars working in highly productive countries (e.g. the United States, China) tend to collaborate with researchers based in their same countries. Finally, current issues and future developments within this scientific area are also discussed.

Includes members:
  Member "arming the public with artificial intelligence to counter social bots":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning")
  Member "deep learning for real time crime forecasting and its ternarization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a comparative study on crime in denver city based on machine learning and data mining")
  Member "transfer learning from deep features for remote sensing and poverty mapping":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bigearthnet dataset with a new class nomenclature for remote sensing image understanding")
  Member "prediction of emerging technologies based on analysis of the us patent citation network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "span core decomposition for temporal networks algorithms and applications")
  Member "mapping excellence in the geography of science an approach based on scopus data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the challenges ahead of spatial scientometrics focusing on the city level")

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.DL(Digital Libraries)