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: "science concierge a fast content based recommendation system for scientific publications"
  Abstract: Finding relevant publications is important for scientists who have to cope with exponentially increasing numbers of scholarly material. Algorithms can help with this task as they help for music, movie, and product recommendations. However, we know little about the performance of these algorithms with scholarly material. Here, we develop an algorithm, and an accompanying Python library, that implements a recommendation system based on the content of articles. Design principles are to adapt to new content, provide near-real time suggestions, and be open source. We tested the library on 15K posters from the Society of Neuroscience Conference 2015. Human curated topics are used to cross validate parameters in the algorithm and produce a similarity metric that maximally correlates with human judgments. We show that our algorithm significantly outperformed suggestions based on keywords. The work presented here promises to make the exploration of scholarly material faster and more accurate.

Connected through hyperedge HE-1 (induced by source paper "automating the search for a patent s prior art with a full text similarity search"):
  with members:
    - "patent retrieval a literature review"
    - "interactive exploration and discovery of scientific publications with pubvis"
    - "what is relevant in a text document an interpretable machine learning approach"
    - "explaining predictions of non linear classifiers in nlp"
    - "textual spatial cosine similarity"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "the effect of novelty on the future impact of scientific grants"):
  with members:
    - "science and technology advance through surprise"

Connected through hyperedge HE-3 (induced by source paper "using text analysis to quantify the similarity and evolution of scientific disciplines"):
  with members:
    - "the ground truth about metadata and community detection in networks"
    - "using network science and text analytics to produce surveys in a scientific topic"
    - "inheritance patterns in citation networks reveal scientific memes"
    - "quantifying long term scientific impact"
    - "characterizing interdisciplinarity of researchers and research topics using web search engines"

Connected through hyperedge HE-4 (induced by source paper "knowledge discovery through text based similarity searches for astronomy literature"):
  with members:
    - "growth rates of modern science a bibliometric analysis based on the number of publications and cited references"

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.IR(Information Retrieval)