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: "nseen neural semantic embedding for entity normalization"
  Abstract: Much of human knowledge is encoded in the text, such as scientific publications, books, and the web. Given the rapid growth of these resources, we need automated methods to extract such knowledge into formal, machine-processable structures, such as knowledge graphs. An important task in this process is entity normalization (also called entity grounding, or resolution), which consists of mapping entity mentions in text to canonical entities in well-known reference sets. However, entity resolution is a challenging problem, since there often are many textual forms for a canonical entity. The problem is particularly acute in the scientific domain, such as biology. For example, a protein may have many different names and syntactic variations on these names. To address this problem, we have developed a general, scalable solution based on a deep Siamese neural network model to embed the semantic information about the entities, as well as their syntactic variations. We use these embeddings for fast mapping of new entities to large reference sets, and empirically show the effectiveness of our framework in challenging bio-entity normalization datasets.

Connected through hyperedge HE-1 (induced by source paper "biomedical named entity recognition via reference set augmented bootstrapping"):
  with members:
    - "learning named entity tagger using domain specific dictionary"
    - "automated phrase mining from massive text corpora"
    - "snorkel rapid training data creation with weak supervision"
    - "swellshark a generative model for biomedical named entity recognition without labeled data"
    - "empower sequence labeling with task aware neural language model"
    - "neural architectures for named entity recognition"
    - "training deep neural networks on noisy labels with bootstrapping"
    - "analysis of semi supervised learning with the yarowsky algorithm"

Connected through hyperedge HE-2 (induced by source paper "unsupervised construction of knowledge graphs from text and code"):
  with members:
    - "mapping text to knowledge graph entities using multi sense lstms"
    - "a survey of machine learning for big code and naturalness"
    - "an overview of embedding models of entities and relationships for knowledge base completion"
    - "efficient estimation of word representations in vector space"

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)