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: "hierarchy of knowledge translation from health problems to ad hoc drug design"
  Abstract: An innovative approach to analyze the complexity of translating novel molecular entities and nanomaterials into pharmaceutical alternatives (i.e., knowledge translation, KT) is discussed. First, some key concepts on the organization and translation of the biomedical knowledge (paradigms, homophily, power law distributions, hierarchy, modularity, and research fronts) are reviewed. Then, we propose a model for the knowledge translation (KT) in Drug Discovery that considers the complexity of interdisciplinary communication. Specifically, we address two highly relevant aspects: 1) A successful KT requires the emergence of organized bodies of inter-and transdisciplinary research, and 2) The hierarchical and modular topological organization of these bodies of knowledge. We focused on a set of previously-published studies on KT which rely on a combination of network analysis and computer-assisted analysis of the contents of scientific literature and patents. The selected studies provide a duo of complementary perspectives: the demand of knowledge (cervical cancer and Ebola hemorrhagic fever) and the supply of knowledge (liposomes and nanoparticles to treat cancer and the paradigmatic Doxil, the first nanodrug to be approved).

Connected through hyperedge HE-1 (induced by source paper "the emergence and evolution of the research fronts in hiv aids research"):
  with members:
    - "hegemonic structure of basic clinical and patented knowledge on ebola research a us army reductionist initiative"
    - "power laws in citation distributions evidence from scopus"

Connected through hyperedge HE-2 (induced by source paper "the structure and dynamics of the research fronts in hiv aids research part whole and adaptive specialization"):
  with members:
    - "hegemonic structure of basic clinical and patented knowledge on ebola research a us army reductionist initiative"
    - "power laws in citation distributions evidence from scopus"

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.SI(Social and Information Networks)