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 "knowledge based biomedical data science 2019"):
  Abstract: Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.

Includes members:
  Member "graph embedding on biomedical networks methods applications and evaluations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "openbiolink a benchmarking framework for large scale biomedical link prediction")
  Member "long tail relation extraction via knowledge graph embeddings and graph convolution networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "open knowledge enrichment for long tail entities")
  Member "constructing large scale biomedical knowledge bases from scratch with rapid annotation of interpretable patterns":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gextext unsupervised knowledge modelling in biomedical literature")
  Member "medical knowledge embedding based on recursive neural network for multi disease diagnosis":
    also connected through other hyperedges:
      (none)
  Member "opa2vec combining formal and informal content of biomedical ontologies to improve similarity based prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bioconceptvec creating and evaluating literature based biomedical concept embeddings on a large scale")

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.AI(Artificial Intelligence)