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: "will this paper increase your h index scientific impact prediction"
  Abstract: Scientific impact plays a central role in the evaluation of the output of scholars, departments, and institutions. A widely used measure of scientific impact is citations, with a growing body of literature focused on predicting the number of citations obtained by any given publication. The effectiveness of such predictions, however, is fundamentally limited by the power-law distribution of citations, whereby publications with few citations are extremely common and publications with many citations are relatively rare. Given this limitation, in this work we instead address a related question asked by many academic researchers in the course of writing a paper, namely: "Will this paper increase my h-index?" Using a real academic dataset with over 1.7 million authors, 2 million papers, and 8 million citation relationships from the premier online academic service ArnetMiner, we formalize a novel scientific impact prediction problem to examine several factors that can drive a paper to increase the primary author's h-index. We find that the researcher's authority on the publication topic and the venue in which the paper is published are crucial factors to the increase of the primary author's h-index, while the topic popularity and the co-authors' h-indices are of surprisingly little relevance. By leveraging relevant factors, we find a greater than 87.5% potential predictability for whether a paper will contribute to an author's h-index within five years. As a further experiment, we generate a self-prediction for this paper, estimating that there is a 76% probability that it will contribute to the h-index of the co-author with the highest current h-index in five years. We conclude that our findings on the quantification of scientific impact can help researchers to expand their influence and more effectively leverage their 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:
    - "can scientific impact be predicted"
    - "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"
    - "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 authors citation counts and h indices with a neural network"):
  with members:
    - "identifying the academic rising stars"
    - "a review of the literature on citation impact indicators"

Connected through hyperedge HE-3 (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"
    - "can scientific impact be predicted"
    - "distributed representations of words and phrases and their compositionality"
    - "pagerank for ranking authors in co citation networks"

Connected through hyperedge HE-4 (induced by source paper "inspiration or preparation explaining creativity in scientific enterprise"):
  with members:
    - "the research space using the career paths of scholars to predict the evolution of the research output of individuals institutions and nations"
    - "the child is father of the man foresee the success at the early stage"
    - "new measures for evaluating creativity in scientific publications"
    - "modeling creativity case studies in python"
    - "can cascades be predicted"
    - "quantifying long term scientific impact"

Connected through hyperedge HE-5 (induced by source paper "can scientific impact be predicted"):
  with members:
    - "modeling and predicting popularity dynamics via reinforced poisson processes"
    - "can cascades be predicted"
    - "quantifying long term scientific impact"
    - "citing for high impact"
    - "information diffusion in computer science citation networks"

Connected through hyperedge HE-6 (induced by source paper "s index towards better metrics for quantifying research impact"):
  with members:
    - "quantifying long term scientific impact"
    - "citing for high impact"

Connected through hyperedge HE-7 (induced by source paper "the child is father of the man foresee the success at the early stage"):
  with members:
    - "quantifying long term scientific impact"

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)