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: "on2vec embedding based relation prediction for ontology population"
  Abstract: Populating ontology graphs represents a long-standing problem for the Semantic Web community. Recent advances in translation-based graph embedding methods for populating instance-level knowledge graphs lead to promising new approaching for the ontology population problem. However, unlike instance-level graphs, the majority of relation facts in ontology graphs come with comprehensive semantic relations, which often include the properties of transitivity and symmetry, as well as hierarchical relations. These comprehensive relations are often too complex for existing graph embedding methods, and direct application of such methods is not feasible. Hence, we propose On2Vec, a novel translation-based graph embedding method for ontology population. On2Vec integrates two model components that effectively characterize comprehensive relation facts in ontology graphs. The first is the Component-specific Model that encodes concepts and relations into low-dimensional embedding spaces without a loss of relational properties; the second is the Hierarchy Model that performs focused learning of hierarchical relation facts. Experiments on several well-known ontology graphs demonstrate the promising capabilities of On2Vec in predicting and verifying new relation facts. These promising results also make possible significant improvements in related methods.

Connected through hyperedge HE-1 (induced by source paper "a neural architecture for person ontology population"):
  with members:
    - "position aware graph neural networks"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "neural fine grained entity type classification with hierarchy aware loss"
    - "ultra fine entity typing"
    - "fine grained classification of personal data entities"
    - "universal sentence encoder"
    - "finer grained entity typing with typenet"
    - "fine grained entity type classification by jointly learning representations and label embeddings"

Connected through hyperedge HE-2 (induced by source paper "improving relation extraction with knowledge attention"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "position aware self attention with relative positional encodings for slot filling"
    - "multi level structured self attentions for distantly supervised relation extraction"
    - "improved neural relation detection for knowledge base question answering"
    - "neural natural language inference models enhanced with external knowledge"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "embedding uncertain knowledge graphs"):
  with members:
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "knowledge base completion baselines strike back"
    - "convolutional 2d knowledge graph embeddings"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "proje embedding projection for knowledge graph completion"
    - "complex embeddings for simple link prediction"
    - "locally adaptive translation for knowledge graph embedding"

Connected through hyperedge HE-4 (induced by source paper "dolores deep contextualized knowledge graph embeddings"):
  with members:
    - "deep contextualized word representations"
    - "kbgan adversarial learning for knowledge graph embeddings"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "poincar e embeddings for learning hierarchical representations"
    - "convolutional 2d knowledge graph embeddings"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"
    - "node2vec scalable feature learning for networks"
    - "chains of reasoning over entities relations and text using recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "neural article pair modeling for wikipedia sub article matching"):
  with members:
    - "co training embeddings of knowledge graphs and entity descriptions for cross lingual entity alignment"
    - "reading wikipedia to answer open domain questions"
    - "text understanding with the attention sum reader network"
    - "multilingual knowledge graph embeddings for cross lingual knowledge alignment"
    - "gated attention readers for text comprehension"
    - "joint learning of the embedding of words and entities for named entity disambiguation"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "convolutional neural network architectures for matching natural language sentences"

Connected through hyperedge HE-6 (induced by source paper "co training embeddings of knowledge graphs and entity descriptions for cross lingual entity alignment"):
  with members:
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "structured attention networks"
    - "cross lingual entity alignment via joint attribute preserving embedding"
    - "convolutional 2d knowledge graph embeddings"
    - "xgboost a scalable tree boosting system"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "multilingual knowledge graph embeddings for cross lingual knowledge alignment"
    - "locally adaptive translation for knowledge graph embedding"

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.AI(Artificial Intelligence)