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: "prediction of emerging technologies based on analysis of the us patent citation network"
  Abstract: The network of patents connected by citations is an evolving graph, which provides a representation of the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. A methodology presented here (1) identifies actual clusters of patents: i.e., technological branches, and (2) gives predictions about the temporal changes of the structure of the clusters. A predictor, called the citation vector, is defined for characterizing technological development to show how a patent cited by other patents belongs to various industrial fields. The clustering technique adopted is able to detect the new emerging recombinations, and predicts emerging new technology clusters. The predictive ability of our new method is illustrated on the example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of patents is determined based on citation data up to 1991, which shows significant overlap of the class 442 formed at the beginning of 1997. These new tools of predictive analytics could support policy decision making processes in science and technology, and help formulate recommendations for action.

Connected through hyperedge HE-1 (induced by source paper "span core decomposition for temporal networks algorithms and applications"):
  with members:
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "a survey of community search over big graphs"
    - "finding events in temporal networks segmentation meets densest subgraph discovery"
    - "graph embedding techniques applications and performance a survey"
    - "on finding dense common subgraphs"
    - "listing all maximal k plexes in temporal graphs"
    - "mining maximal span cores from temporal networks"
    - "to be connected or not to be connected that is the minimum inefficiency subgraph problem"

Connected through hyperedge HE-2 (induced by source paper "where are we using scopus to map the literature at the intersection between artificial intelligence and crime"):
  with members:
    - "arming the public with artificial intelligence to counter social bots"
    - "deep learning for real time crime forecasting and its ternarization"
    - "transfer learning from deep features for remote sensing and poverty mapping"
    - "mapping excellence in the geography of science an approach based on scopus data"

Connected through hyperedge HE-3 (induced by source paper "deep technology tracing for high tech companies"):
  with members:
    - "ekt exercise aware knowledge tracing for student performance prediction"
    - "a primer on neural network models for natural language processing"
    - "speech recognition with deep recurrent neural networks"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-4 (induced by source paper "mining maximal span cores from temporal networks"):
  with members:
    - "best friends forever bff finding lasting dense subgraphs"
    - "enumerating maximal cliques in temporal graphs"
    - "computing maximal cliques in link streams"
    - "social resilience in online communities the autopsy of friendster"
    - "dense subgraph maintenance under streaming edge weight updates for real time story identification"
    - "listing all maximal cliques in sparse graphs in near optimal time"

Connected through hyperedge HE-5 (induced by source paper "predictive effects of novelty measured by temporal embeddings on the growth of scientific literature"):
  with members:
    - "what is an emerging technology"
    - "growth rates of modern science a bibliometric analysis based on the number of publications and cited references"
    - "towards an explanatory and computational theory of scientific discovery"

Connected through hyperedge HE-6 (induced by source paper "dynamic patterns of knowledge flows across technological domains empirical results and link prediction"):
  with members:
    - "tracing technological development trajectories a genetic knowledge persistence based main path approach"
    - "modeling of technological performance trends using design theory"

Connected through hyperedge HE-7 (induced by source paper "quantifying the changing role of past publications"):
  with members:
    - "scientometrics untangling the topics"
    - "the structure and dynamics of co citation clusters a multiple perspective co citation analysis"

Connected through hyperedge HE-8 (induced by source paper "recognition of emerging technology trends class selective study of citations in the u s patent citation network"):
  with members:
    - "pagerank for ranking authors in co citation 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)