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: "the landscape of neuroimage ing research"
  Abstract: Abstract   As the field of neuroimaging grows, it can be difficult for scientists within the field to gain and maintain a detailed understanding of its ever-changing landscape. While collaboration and citation networks highlight important contributions within the field, the roles of and relations among specific areas of study can remain quite opaque. Here, we apply techniques from network science to map the landscape of neuroimaging research documented in the journal NeuroImage over the past decade. We create a network in which nodes represent research topics, and edges give the degree to which these topics tend to be covered in tandem. The network displays small-world architecture, with communities characterized by common imaging modalities and medical applications, and with hubs that integrate these distinct subfields. Using node-level analysis, we quantify the structural roles of individual topics within the neuroimaging landscape, and find high levels of clustering within the structural MRI subfield as well as increasing participation among topics related to psychiatry. The overall prevalence of a topic is unrelated to the prevalence of its neighbors, but the degree to which a topic becomes more or less popular over time is strongly related to changes in the prevalence of its neighbors. Finally, we incorporate data from PNAS to investigate whether it serves as a trend-setter for topics’ use within NeuroImage. We find that popularity trends are correlated across the two journals, and that changes in popularity tend to occur earlier within PNAS among growing topics. Broadly, this work presents a cohesive model for understanding the emergent relationships and dynamics of research topics within NeuroImage.

Connected through hyperedge HE-1 (induced by source paper "predicting research trends with semantic and neural networks with an application in quantum physics"):
  with members:
    - "the emergent integrated network structure of scientific research"
    - "a comprehensive survey on graph neural networks"
    - "attention is all you need"
    - "gated graph sequence neural networks"
    - "learning convolutional neural networks for graphs"
    - "quantifying the cognitive extent of science"
    - "inheritance patterns in citation networks reveal scientific memes"
    - "coauthorship and citation patterns in the physical review"

Connected through hyperedge HE-2 (induced by source paper "the emergent integrated network structure of scientific research"):
  with members:
    - "multiresolution consensus clustering in networks"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "quantifying long term scientific impact"
    - "assessing the consistency of community structure in complex networks"

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)