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: "citing for high impact"
  Abstract: The question of citation behavior has always intrigued scientists from various disciplines. While general citation patterns have been widely studied in the literature we develop the notion of citation projection graphs by investigating the citations among the publications that a given paper cites. We investigate how patterns of citations vary between various scientific disciplines and how such patterns reflect the scientific impact of the paper. We find that idiosyncratic citation patterns are characteristic for low impact papers; while narrow, discipline-focused citation patterns are common for medium impact papers. Our results show that crossing-community, or bridging citation patters are high risk and high reward since such patterns are characteristic for both low and high impact papers. Last, we observe that recently citation networks are trending toward more bridging and interdisciplinary forms.

Connected through hyperedge HE-1 (induced by source paper "co citations in context disciplinary heterogeneity is relevant"):
  with members:
    - "from louvain to leiden guaranteeing well connected communities"
    - "which type of citation analysis generates the most accurate taxonomy of scientific and technical knowledge"
    - "research portfolio analysis and topic prominence"
    - "measuring contextual citation impact of scientific journals"

Connected through hyperedge HE-2 (induced by source paper "from academia to software development publication citations in source code comments"):
  with members:
    - "a survey on recent advances in named entity recognition from deep learning models"
    - "9 6 million links in source code comments purpose evolution and decay"

Connected through hyperedge HE-3 (induced by source paper "cross language citation recommendation via hierarchical representation learning on heterogeneous graph"):
  with members:
    - "a deep relevance matching model for ad hoc retrieval"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "neural machine translation by jointly learning to align and translate"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "can scientific impact be predicted"):
  with members:
    - "modeling and predicting popularity dynamics via reinforced poisson processes"
    - "can cascades be predicted"
    - "will this paper increase your h index scientific impact prediction"
    - "quantifying long term scientific impact"
    - "information diffusion in computer science citation networks"

Connected through hyperedge HE-5 (induced by source paper "citation classification for behavioral analysis of a scientific field"):
  with members:
    - "cascaded grammatical relation assignment"

Connected through hyperedge HE-6 (induced by source paper "s index towards better metrics for quantifying research impact"):
  with members:
    - "will this paper increase your h index scientific impact prediction"
    - "quantifying long term scientific impact"

Connected through hyperedge HE-7 (induced by source paper "incremental algorithms for network management and analysis based on closeness centrality"):
  with members:
    - "betweenness centrality algorithms and lower bounds"
    - "fast approximation of centrality"

Connected through hyperedge HE-8 (induced by source paper "development of computer science disciplines a social network analysis approach"):
  with members:
    - "communities and hierarchical structures in dynamic social networks analysis and visualization"
    - "a principal component analysis of 39 scientific impact measures"

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.DL(Digital Libraries)