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: "can scientific impact be predicted"
  Abstract: A widely used measure of scientific impact is citations. However, due to their heavy-tailed distribution, citations are fundamentally difficult to predict. Instead, to characterize scientific impact, we address two analogous questions asked by many scientific researchers: “How will my  h -index evolve over time, and which of my previously or newly published papers will contribute to it?” To answer these questions, we perform two related tasks. First, we develop a model to predict authors’ future  h -indices based on their current scientific impact. Second, we examine the factors that drive papers—either previously or newly published—to increase their authors’ predicted future  h -indices. By leveraging relevant factors, we can predict an author's  h -index in five years with an    $R^2$       value of 0.92 and whether a previously (newly) published paper will contribute to this future  h -index with an   $F_1$       score of 0.99 (0.77). We find that topical authority and publication venue are crucial to these effective predictions, while topic popularity is surprisingly inconsequential. Further, we develop an online tool that allows users to generate informed  h  -index predictions. Our work demonstrates the predictability of scientific impact, and can help researchers to effectively leverage their scholarly position of “standing on the shoulders of giants.”

Connected through hyperedge HE-1 (induced by source paper "predicting citation counts based on deep neural network learning techniques"):
  with members:
    - "deep residual learning for image recognition"
    - "a review of the characteristics of 108 author level bibliometric indicators"
    - "very deep convolutional networks for large scale image recognition"
    - "bibliometric indicators of young authors in astrophysics can later stars be predicted"
    - "neural machine translation by jointly learning to align and translate"
    - "will this paper increase your h index scientific impact prediction"
    - "deep visual semantic alignments for generating image descriptions"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-2 (induced by source paper "predicting publication productivity for researchers a latent variable model"):
  with members:
    - "predicting citation counts based on deep neural network learning techniques"
    - "predicting publication productivity for researchers a piecewise poisson model"
    - "predicting publication long term impact through a combination of early citations and journal impact factor"
    - "how to improve the prediction based on citation impact percentiles for years shortly after the publication date"
    - "quantifying long term scientific impact"

Connected through hyperedge HE-3 (induced by source paper "predicting publication productivity for researchers a piecewise poisson model"):
  with members:
    - "predicting citation counts based on deep neural network learning techniques"
    - "predicting publication long term impact through a combination of early citations and journal impact factor"
    - "accuracy of simple initials based methods for author name disambiguation"
    - "how to improve the prediction based on citation impact percentiles for years shortly after the publication date"

Connected through hyperedge HE-4 (induced by source paper "an ai aid to the editors exploring the possibility of an ai assisted article classification system"):
  with members:
    - "influence of reviewer interaction network on long term citations a case study of the scientific peer review system of the journal of high energy physics"
    - "will this paper increase your h index scientific impact prediction"
    - "distributed representations of words and phrases and their compositionality"
    - "pagerank for ranking authors in co citation networks"

Connected through hyperedge HE-5 (induced by source paper "cloud based big data management and analytics for scholarly resources current trends challenges and scope for future research"):
  with members:
    - "grand challenges in altmetrics heterogeneity data quality and dependencies"
    - "content based book recommending using learning for text categorization"

Connected through hyperedge HE-6 (induced by source paper "architecting time critical big data systems"):
  with members:
    - "scheduling storms and streams in the cloud"
    - "towards real time detection and tracking of spatio temporal features blob filaments in fusion plasma"

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)