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: "locally adaptive translation for knowledge graph embedding"
  Abstract: Knowledge graph embedding aims to represent entities and relations in a large-scale knowledge graph as elements in a continuous vector space. Existing methods, e.g., TransE and TransH, learn embedding representation by defining a global margin-based loss function over the data. However, the optimal loss function is determined during experiments whose parameters are examined among a closed set of candidates. Moreover, embeddings over two knowledge graphs with different entities and relations share the same set of candidate loss functions, ignoring the locality of both graphs. This leads to the limited performance of embedding related applications. In this paper, we propose a locally adaptive translation method for knowledge graph embedding, called TransA, to find the optimal loss function by adaptively determining its margin over different knowledge graphs. Experiments on two benchmark data sets demonstrate the superiority of the proposed method, as compared to the-state-of-the-art ones.

Connected through hyperedge HE-1 (induced by source paper "kgvec2go knowledge graph embeddings as a service"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "holographic embeddings of knowledge graphs"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "a survey on knowledge graph embeddings with literals which model links better literal ly"):
  with members:
    - "incorporating literals into knowledge graph embeddings"
    - "co training embeddings of knowledge graphs and entity descriptions for cross lingual entity alignment"
    - "embedding multimodal relational data for knowledge base completion"
    - "a first experiment on including text literals in kglove"
    - "kblrn end to end learning of knowledge base representations with latent relational and numerical features"
    - "multi task neural network for non discrete attribute prediction in knowledge graphs"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "chains of reasoning over entities relations and text using recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "neural markov logic networks"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "differentiable reasoning on large knowledge bases and natural language"
    - "lyrics a general interface layer to integrate ai and deep learning"
    - "deepproblog neural probabilistic logic programming"
    - "inference learning and population size projectivity for srl models"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-4 (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"
    - "an interpretable knowledge transfer model for knowledge base completion"
    - "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"
    - "from one point to a manifold knowledge graph embedding for precise link prediction"
    - "holographic embeddings of knowledge graphs"

Connected through hyperedge HE-5 (induced by source paper "defeats gan a simpler model outperforms in knowledge representation learning"):
  with members:
    - "proje embedding projection for knowledge graph completion"
    - "adam a method for stochastic optimization"
    - "a semantic matching energy function for learning with multi relational data"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "knowledge graph development for app store data modeling"):
  with members:
    - "androvault constructing knowledge graph from millions of android apps for automated analysis"

Connected through hyperedge HE-7 (induced by source paper "embedding uncertain knowledge graphs"):
  with members:
    - "on2vec embedding based relation prediction for ontology population"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "co training embeddings of knowledge graphs and entity descriptions for cross lingual entity alignment"):
  with members:
    - "on2vec embedding based relation prediction for ontology population"
    - "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"

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)