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: "proje embedding projection for knowledge graph completion"
  Abstract: With the large volume of new information created every day, determining the validity of information in a knowledge graph and filling in its missing parts are crucial tasks for many researchers and practitioners. To address this challenge, a number of knowledge graph completion methods have been developed using low-dimensional graph embeddings. Although researchers continue to improve these models using an increasingly complex feature space, we show that simple changes in the architecture of the underlying model can outperform state-of-the-art models without the need for complex feature engineering. In this work, we present a shared variable neural network model called ProjE that fills-in missing information in a knowledge graph by learning joint embeddings of the knowledge graph's entities and edges, and through subtle, but important, changes to the standard loss function. In doing so, ProjE has a parameter size that is smaller than 11 out of 15 existing methods while performing $37\%$ better than the current-best method on standard datasets. We also show, via a new fact checking task, that ProjE is capable of accurately determining the veracity of many declarative statements.

Connected through hyperedge HE-1 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs"):
  with members:
    - "a survey on knowledge graphs representation acquisition and applications"
    - "knowledge graph embedding for link prediction a comparative analysis"
    - "multi view knowledge graph embedding for entity alignment"
    - "cross lingual knowledge graph alignment via graph matching neural network"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "jointly learning entity and relation representations for entity alignment"
    - "analyzing the limitations of cross lingual word embedding mappings"
    - "learning to exploit long term relational dependencies in knowledge graphs"

Connected through hyperedge HE-2 (induced by source paper "realistic re evaluation of knowledge graph completion methods an experimental study"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "tucker tensor factorization for knowledge graph completion"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "on evaluating embedding models for knowledge base completion"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "convolutional 2d knowledge graph embeddings"
    - "complex embeddings for simple link prediction"

Connected through hyperedge HE-3 (induced by source paper "customized graph embedding tailoring embedding vectors to different applications"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "darts differentiable architecture search"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "node2vec scalable feature learning for networks"
    - "learning convolutional neural networks for graphs"
    - "discriminative embeddings of latent variable models for structured data"
    - "revisiting semi supervised learning with graph embeddings"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-4 (induced by source paper "error detection in knowledge graphs path ranking embeddings or both"):
  with members:
    - "logic rules powered knowledge graph embedding"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "does william shakespeare really write hamlet knowledge representation learning with confidence"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "complex embeddings for simple link prediction"
    - "holographic embeddings of knowledge graphs"
    - "learning multi relational semantics using neural embedding models"

Connected through hyperedge HE-5 (induced by source paper "learning to exploit long term relational dependencies in knowledge graphs"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "representation learning on graphs with jumping knowledge networks"
    - "co training embeddings of knowledge graphs and entity descriptions for cross lingual entity alignment"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "cross lingual entity alignment via joint attribute preserving embedding"
    - "convolutional 2d knowledge graph embeddings"
    - "node2vec scalable feature learning for networks"
    - "chains of reasoning over entities relations and text using recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "kg bert bert for knowledge graph completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "ernie enhanced language representation with informative entities"
    - "a multiscale visualization of attention in the transformer model"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "dolores deep contextualized knowledge graph embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "relation embedding with dihedral group in knowledge graph"):
  with members:
    - "understanding straight through estimator in training activation quantized neural nets"
    - "learning to exploit long term relational dependencies in knowledge graphs"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "toruse knowledge graph embedding on a lie group"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "knowledge base completion baselines strike back"

Connected through hyperedge HE-8 (induced by source paper "assessing the lexico semantic relational knowledge captured by word and concept embeddings"):
  with members:
    - "not just about size a study on the role of distributed word representations in the analysis of scientific publications"
    - "a decomposable attention model for natural language inference"
    - "swivel improving embeddings by noticing what s missing"
    - "enriching word vectors with subword information"
    - "path based vs distributional information in recognizing lexical semantic relations"
    - "visualizing and understanding neural models in nlp"
    - "experiments with three approaches to recognizing lexical entailment"
    - "on the properties of neural machine translation encoder decoder approaches"

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)