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: "learning new facts from knowledge bases with neural tensor networks and semantic word vectors"
  Abstract: Knowledge bases provide applications with the benefit of easily accessible, systematic relational knowledge but often suffer in practice from their incompleteness and lack of knowledge of new entities and relations. Much work has focused on building or extending them by finding patterns in large unannotated text corpora. In contrast, here we mainly aim to complete a knowledge base by predicting additional true relationships between entities, based on generalizations that can be discerned in the given knowledgebase. We introduce a neural tensor network (NTN) model which predicts new relationship entries that can be added to the database. This model can be improved by initializing entity representations with word vectors learned in an unsupervised fashion from text, and when doing this, existing relations can even be queried for entities that were not present in the database. Our model generalizes and outperforms existing models for this problem, and can classify unseen relationships in WordNet with an accuracy of 75.8%.

Connected through hyperedge HE-1 (induced by source paper "deep learning approach on information diffusion in heterogeneous networks"):
  with members:
    - "coupled deep learning for heterogeneous face recognition"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "node2vec scalable feature learning for networks"
    - "deepcas an end to end predictor of information cascades"
    - "information diffusion in social networks in two phases"
    - "revisiting semi supervised learning with graph embeddings"
    - "spreading processes in multilayer networks"
    - "a survey of heterogeneous information network analysis"

Connected through hyperedge HE-2 (induced by source paper "using local knowledge graph construction to scale seq2seq models to multi document inputs"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "multi news a large scale multi document summarization dataset and abstractive hierarchical model"
    - "transformer xl attentive language models beyond a fixed length context"
    - "lattice cnns for matching based chinese question answering"
    - "adaptive attention span in transformers"
    - "eli5 long form question answering"
    - "graph neural networks a review of methods and applications"
    - "building dynamic knowledge graphs from text using machine reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "fact discovery from knowledge base via facet decomposition"):
  with members:
    - "analogical inference for multi relational embeddings"
    - "probabilistic reasoning via deep learning neural association models"
    - "complex embeddings for simple link prediction"
    - "modeling relation paths for representation learning of knowledge bases"
    - "holographic embeddings of knowledge graphs"
    - "adam a method for stochastic optimization"
    - "question answering with subgraph embeddings"
    - "embedding entities and relations for learning and inference in knowledge bases"

Connected through hyperedge HE-4 (induced by source paper "medical knowledge embedding based on recursive neural network for multi disease diagnosis"):
  with members:
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"
    - "gram graph based attention model for healthcare representation learning"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "a semantic matching energy function for learning with multi relational data"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-5 (induced by source paper "the lifted matrix space model for semantic composition"):
  with members:
    - "structural attention neural networks for improved sentiment analysis"
    - "natural language inference over interaction space"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "neural semantic encoders"
    - "aligning packed dependency trees a theory of composition for distributional semantics"
    - "long short term memory over tree structures"
    - "compositional distributional semantics with long short term memory"

Connected through hyperedge HE-6 (induced by source paper "analogical inference for multi relational embeddings"):
  with members:
    - "cross graph learning of multi relational associations"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"
    - "modeling relation paths for representation learning of knowledge bases"
    - "holographic embeddings of knowledge graphs"
    - "traversing knowledge graphs in vector space"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "distributed representations of words and phrases and their compositionality"
    - "wikipedia based semantic interpretation for natural language processing"

Connected through hyperedge HE-7 (induced by source paper "insights into analogy completion from the biomedical domain"):
  with members:
    - "issues in evaluating semantic spaces using word analogies"
    - "towards universal paraphrastic sentence embeddings"
    - "efficient estimation of word representations in vector space"
    - "from frequency to meaning vector space models of semantics"
    - "bagpack a general framework to represent semantic relations"
    - "a uniform approach to analogies synonyms antonyms and associations"

Connected through hyperedge HE-8 (induced by source paper "cross lingual entity alignment via joint attribute preserving embedding"):
  with members:
    - "multilingual knowledge graph embeddings for cross lingual knowledge alignment"
    - "type constrained representation learning in knowledge graphs"
    - "efficient estimation of word representations in vector space"
    - "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)