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: "inferring missing entity type instances for knowledge base completion new dataset and methods"
  Abstract: Most of previous work in knowledge base (KB) completion has focused on the problem of relation extraction. In this work, we focus on the task of inferring missing entity type instances in a KB, a fundamental task for KB competition yet receives little attention. Due to the novelty of this task, we construct a large-scale dataset and design an automatic evaluation methodology. Our knowledge base completion method uses information within the existing KB and external information from Wikipedia. We show that individual methods trained with a global objective that considers unobserved cells from both the entity and the type side gives consistently higher quality predictions compared to baseline methods. We also perform manual evaluation on a small subset of the data to verify the effectiveness of our knowledge base completion methods and the correctness of our proposed automatic evaluation method.

Connected through hyperedge HE-1 (induced by source paper "modelling semantic categories using conceptual neighborhood"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dissecting contextual word embeddings architecture and representation"
    - "learning conceptual space representations of interrelated concepts"
    - "embedding words and senses together via joint knowledge enhanced training"
    - "joint embedding of hierarchical categories and entities for concept categorization and dataless classification"
    - "nonparametric spherical topic modeling with word embeddings"

Connected through hyperedge HE-2 (induced by source paper "knowledge representation learning a quantitative review"):
  with members:
    - "improving visual relationship detection using semantic modeling of scene descriptions"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "flexible end to end dialogue system for knowledge grounded conversation"
    - "a neural knowledge language model"
    - "analogical inference for multi relational embeddings"
    - "visual translation embedding network for visual relation detection"
    - "probabilistic reasoning via deep learning neural association models"

Connected through hyperedge HE-3 (induced by source paper "generating fine grained open vocabulary entity type descriptions"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "get to the point summarization with pointer generator networks"
    - "supervised typing of big graphs using semantic embeddings"
    - "challenges in data to document generation"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "video captioning with multi faceted attention"
    - "generating text from structured data with application to the biography domain"

Connected through hyperedge HE-4 (induced by source paper "representation learning of entities and documents from knowledge base descriptions"):
  with members:
    - "corpus level fine grained entity typing using contextual information"
    - "embedding words and senses together via joint knowledge enhanced training"
    - "joint learning of the embedding of words and entities for named entity disambiguation"
    - "skip thought vectors"
    - "towards universal paraphrastic sentence embeddings"
    - "learning to understand phrases by embedding the dictionary"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-5 (induced by source paper "hierarchical losses and new resources for fine grained entity typing and linking"):
  with members:
    - "attention is all you need"
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "poincar e embeddings for learning hierarchical representations"
    - "constructing datasets for multi hop reading comprehension across documents"
    - "capturing semantic similarity for entity linking with convolutional neural networks"
    - "generalizing to unseen entities and entity pairs with row less universal schema"
    - "noise mitigation for neural entity typing and relation extraction"
    - "complex embeddings for simple link prediction"

Connected through hyperedge HE-6 (induced by source paper "multi multi view learning multilingual and multi representation entity typing"):
  with members:
    - "fine grained entity typing with high multiplicity assignments"
    - "an attention based collaboration framework for multi view network representation learning"
    - "enriching word vectors with subword information"
    - "noise mitigation for neural entity typing and relation extraction"
    - "a survey on multi view learning"

Connected through hyperedge HE-7 (induced by source paper "learning conceptual space representations of interrelated concepts"):
  with members:
    - "take and took gaggle and goose book and read evaluating the utility of vector differences for lexical relation learning"
    - "zero shot learning through cross modal transfer"

Connected through hyperedge HE-8 (induced by source paper "a web scale system for scientific knowledge exploration"):
  with members:
    - "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)