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: "graphene a precise biomedical literature retrieval engine with graph augmented deep learning and external knowledge empowerment"
  Abstract: Effective biomedical literature retrieval (BLR) plays a central role inprecision medicine informatics. In this paper, we propose GRAPHENE,which is a deep learning based framework for precise BLR. GRAPHENEconsists of three main different modules 1) graph-augmented doc-ument representation learning; 2) query expansion and represen-tation learning and 3) learning to rank biomedical articles. Thegraph-augmented document representation learning module con-structs a document-concept graph containing biomedical conceptnodes and document nodes so that global biomedical related con-cept from external knowledge source can be captured, which isfurther connected to a BiLSTM so both local and global topics canbe explored. Query expansion and representation learning moduleexpands the query with abbreviations and different names, and thenbuilds a CNN-based model to convolve the expanded query andobtain a vector representation for each query. Learning to rank min-imizes a ranking loss between biomedical articles with the queryto learn the retrieval function. Experimental results on applyingour system to TREC Precision Medicine track data are provided todemonstrate its effectiveness.

Connected through hyperedge HE-1 (induced by source paper "biomedical evidence generation engine"):
  with members:
    - "interactive attention for semantic text matching"

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.IR(Information Retrieval)