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: "opa2vec combining formal and informal content of biomedical ontologies to improve similarity based prediction"
  Abstract: Motivation: Ontologies are widely used in biology for data annotation, integration, and analysis. In addition to formally structured axioms, ontologies contain meta-data in the form of annotation axioms which provide valuable pieces of information that characterize ontology classes. Annotations commonly used in ontologies include class labels, descriptions, or synonyms. Despite being a rich source of semantic information, the ontology meta-data are generally unexploited by ontology-based analysis methods such as semantic similarity measures. Results: We propose a novel method, OPA2Vec, to generate vector representations of biological entities in ontologies by combining formal ontology axioms and annotation axioms from the ontology meta-data. We apply a Word2Vec model that has been pre-trained on PubMed abstracts to produce feature vectors from our collected data. We validate our method in two different ways: first, we use the obtained vector representations of proteins as a similarity measure to predict protein-protein interaction (PPI) on two different datasets. Second, we evaluate our method on predicting gene-disease associations based on phenotype similarity by generating vector representations of genes and diseases using a phenotype ontology, and applying the obtained vectors to predict gene-disease associations. These two experiments are just an illustration of the possible applications of our method. OPA2Vec can be used to produce vector representations of any biomedical entity given any type of biomedical ontology. Availability: this https URL Contact: robert.hoehndorf@kaust.edu.sa and xin.gao@kaust.edu.sa.

Connected through hyperedge HE-1 (induced by source paper "bioconceptvec creating and evaluating literature based biomedical concept embeddings on a large scale"):
  with members:
    - "relation extraction between the clinical entities based on the shortest dependency path based lstm"
    - "biomedical mention disambiguation using a deep learning approach"
    - "reflex flexible framework for relation extraction in multiple domains"
    - "deep learning with sentence embeddings pre trained on biomedical corpora improves the performance of finding similar sentences in electronic medical records"
    - "concept based embeddings for natural language processing"
    - "hierarchical neural networks for sequential sentence classification in medical scientific abstracts"
    - "medical concept embedding with time aware attention"
    - "clinical concept embeddings learned from massive sources of multimodal medical data"

Connected through hyperedge HE-2 (induced by source paper "knowledge based biomedical data science 2019"):
  with members:
    - "graph embedding on biomedical networks methods applications and evaluations"
    - "long tail relation extraction via knowledge graph embeddings and graph convolution networks"
    - "constructing large scale biomedical knowledge bases from scratch with rapid annotation of interpretable patterns"
    - "medical knowledge embedding based on recursive neural network for multi disease diagnosis"

Connected through hyperedge HE-3 (induced by source paper "aligning biomedical metadata with ontologies using clustering and embeddings"):
  with members:
    - "the variable quality of metadata about biological samples used in biomedical experiments"
    - "lexicon infused phrase embeddings for named entity resolution"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"

Connected through hyperedge HE-4 (induced by source paper "el embeddings geometric construction of models for the description logic el"):
  with members:
    - "semantic similarity from natural language and ontology analysis"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

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.CL(Computation and Language)