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: "semantic similarity from natural language and ontology analysis"
  Abstract: Artificial Intelligence federates numerous scientific fields in the aim of developing machines able to assist human operators performing complex treatments; most of which demand high cognitive skills (e.g. learning or decision processes). Central to this quest is to give machines the ability to estimate the likeness or similarity between things in the way human beings estimate the similarity between stimuli. In this context, this book focuses on semantic measures: approaches designed for comparing semantic entities such as units of language, e.g. words, sentences, or concepts and instances defined into knowledge bases. The aim of these measures is to assess the similarity or relatedness of such semantic entities by taking into account their semantics, i.e. their meaning; intuitively, the words tea and coffee, which both refer to stimulating beverage, will be estimated to be more semantically similar than the words toffee (confection) and coffee, despite that the last pair has a higher syntactic similarity. The two state-of-the-art approaches for estimating and quantifying semantic similarities/relatedness of semantic entities are presented in detail: the first one relies on corpora analysis and is based on Natural Language Processing techniques and semantic models while the second is based on more or less formal, computer-readable and workable forms of knowledge such as semantic networks, thesauri or ontologies. Semantic measures are widely used today to compare units of language, concepts, instances or even resources indexed by them (e.g., documents, genes). They are central elements of a large variety of Natural Language Processing applications and knowledge-based treatments, and have therefore naturally been subject to intensive and interdisciplinary research efforts during last decades. Beyond a simple inventory and categorization of existing measures, the aim of this monograph is to convey novices as well as researchers of these domains toward a better understanding of semantic similarity estimation and more generally semantic measures. To this end, we propose an in-depth characterization of existing proposals by discussing their features, the assumptions on which they are based and empirical results regarding their performance in particular applications. By answering these questions and by providing a detailed discussion on the foundations of semantic measures, our aim is to give the reader key knowledge required to: (i) select the more relevant methods according to a particular usage context, (ii) understand the challenges offered to this field of study, (iii) distinguish room of improvements for state-of-the-art approaches and (iv) stimulate creativity toward the development of new approaches. In this aim, several definitions, theoretical and practical details, as well as concrete applications are presented.

Connected through hyperedge HE-1 (induced by source paper "from topic networks to distributed cognitive maps zipfian topic universes in the area of volunteered geographic information"):
  with members:
    - "transfer topic labeling with domain specific knowledge base an analysis of uk house of commons speeches 1935 2014"
    - "extracting and analyzing semantic relatedness between cities using news articles"
    - "bag of tricks for efficient text classification"
    - "collective dynamics of social annotation"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-2 (induced by source paper "covariance and correlation kernels on a graph in the generalized bag of paths formalism"):
  with members:
    - "randomized optimal transport on a graph framework and new distance measures"
    - "network representation learning a survey"
    - "a bag of paths node criticality measure"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "two betweenness centrality measures based on randomized shortest paths"
    - "deepwalk online learning of social representations"
    - "distributed representations of words and phrases and their compositionality"
    - "interpolating between random walks and shortest paths a path functional approach"

Connected through hyperedge HE-3 (induced by source paper "el embeddings geometric construction of models for the description logic el"):
  with members:
    - "opa2vec combining formal and informal content of biomedical ontologies to improve similarity based prediction"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "not just a matter of semantics the relationship between visual similarity and semantic similarity"):
  with members:
    - "hierarchy based image embeddings for semantic image retrieval"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "a survey on deep learning in medical image analysis"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "opa2vec combining formal and informal content of biomedical ontologies to improve similarity based prediction"):
  with members:
    - "aber owl a framework for ontology based data access in biology"
    - "semantic similarity in a taxonomy an information based measure and its application to problems of ambiguity in natural language"

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.AI(Artificial Intelligence)