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: "an interpretable knowledge transfer model for knowledge base completion"
  Abstract: Knowledge bases are important resources for a variety of natural language processing tasks but suffer from incompleteness. We propose a novel embedding model, \emph{ITransF}, to perform knowledge base completion. Equipped with a sparse attention mechanism, ITransF discovers hidden concepts of relations and transfer statistical strength through the sharing of concepts. Moreover, the learned associations between relations and concepts, which are represented by sparse attention vectors, can be interpreted easily. We evaluate ITransF on two benchmark datasets---WN18 and FB15k for knowledge base completion and obtains improvements on both the mean rank and Hits@10 metrics, over all baselines that do not use additional information.

Connected through hyperedge HE-1 (induced by source paper "hyperkg hyperbolic knowledge graph embeddings for knowledge base completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "tucker tensor factorization for knowledge graph completion"
    - "inferring concept hierarchies from text corpora via hyperbolic embeddings"
    - "multi relational poincar e graph embeddings"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "poincar e glove hyperbolic word embeddings"
    - "hyperbolic neural networks"
    - "from knowledge graph embedding to ontology embedding an analysis of the compatibility between vector space representations and rules"

Connected through hyperedge HE-2 (induced by source paper "attention is not explanation"):
  with members:
    - "explainable prediction of medical codes from clinical text"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "deriving machine attention from human rationales"
    - "structured attention networks"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"
    - "attention is all you need"
    - "axiomatic attribution for deep networks"
    - "right for the right reasons training differentiable models by constraining their explanations"

Connected through hyperedge HE-3 (induced by source paper "learning to deceive with attention based explanations"):
  with members:
    - "attention is not not explanation"
    - "on identifiability in transformers"
    - "bias in bios a case study of semantic representation bias in a high stakes setting"
    - "on the validity of self attention as explanation in transformer models"
    - "attention is not explanation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"

Connected through hyperedge HE-4 (induced by source paper "decompressing knowledge graph representations for link prediction"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "on the dimensionality of word embedding"
    - "kbgan adversarial learning for knowledge graph embeddings"
    - "toruse knowledge graph embedding on a lie group"
    - "convolutional 2d knowledge graph embeddings"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"
    - "complex embeddings for simple link prediction"

Connected through hyperedge HE-5 (induced by source paper "domain representation for knowledge graph embedding"):
  with members:
    - "response ranking with deep matching networks and external knowledge in information seeking conversation systems"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"
    - "proje embedding projection for knowledge graph completion"
    - "modeling relation paths for representation learning of knowledge bases"
    - "locally adaptive translation for knowledge graph embedding"
    - "from one point to a manifold knowledge graph embedding for precise link prediction"
    - "holographic embeddings of knowledge graphs"

Connected through hyperedge HE-6 (induced by source paper "representation learning with ordered relation paths for knowledge graph completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "analogical inference for multi relational embeddings"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"
    - "proje embedding projection for knowledge graph completion"
    - "complex embeddings for simple link prediction"
    - "modeling relation paths for representation learning of knowledge bases"
    - "holographic embeddings of knowledge graphs"
    - "traversing knowledge graphs in vector space"

Connected through hyperedge HE-7 (induced by source paper "distributional negative sampling for knowledge base completion"):
  with members:
    - "analysis of the impact of negative sampling on link prediction in knowledge graphs"
    - "convolutional 2d knowledge graph embeddings"
    - "proje embedding projection for knowledge graph completion"
    - "modeling relation paths for representation learning of knowledge bases"
    - "holographic embeddings of knowledge graphs"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "new embedded representations and evaluation protocols for inferring transitive relations"):
  with members:
    - "unsupervised hypernym detection by distributional inclusion vector embedding"
    - "learning distributed representations of texts and entities from knowledge base"
    - "finer grained entity typing with typenet"
    - "poincar e embeddings for learning hierarchical representations"
    - "corpus level fine grained entity typing using contextual information"
    - "an attentive neural architecture for fine grained entity type classification"
    - "complex embeddings for simple link prediction"
    - "entity embeddings with conceptual subspaces as a basis for plausible reasoning"

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)