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: "knowledge base completion with out of knowledge base entities a graph neural network approach"
  Abstract: Knowledge base completion (KBC) aims to predict missing information in a knowledge base.In this paper, we address the out-of-knowledge-base (OOKB) entity problem in KBC:how to answer queries concerning test entities not observed at training time. Existing embedding-based KBC models assume that all test entities are available at training time, making it unclear how to obtain embeddings for new entities without costly retraining. To solve the OOKB entity problem without retraining, we use graph neural networks (Graph-NNs) to compute the embeddings of OOKB entities, exploiting the limited auxiliary knowledge provided at test time.The experimental results show the effectiveness of our proposed model in the OOKB setting.Additionally, in the standard KBC setting in which OOKB entities are not involved, our model achieves state-of-the-art performance on the WordNet dataset. The code and dataset are available at this https URL. This paper has been accepted by IJCAI17.

Connected through hyperedge HE-1 (induced by source paper "continuous graph flow for flexible density estimation"):
  with members:
    - "layoutvae stochastic scene layout generation from a label set"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "graph to sequence learning using gated graph neural networks"
    - "neural ordinary differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "image generation from scene graphs"
    - "image generation from layout"

Connected through hyperedge HE-2 (induced by source paper "knowledge infused learning k il towards deep incorporation of knowledge in deep learning"):
  with members:
    - "infusing domain knowledge in ai based black box models for better explainability with application in bankruptcy prediction"
    - "ernie enhanced representation through knowledge integration"
    - "predictive analysis on twitter techniques and applications"
    - "knowledge aware conversation generation with explainable reasoning on augmented graphs"
    - "modeling islamist extremist communications on social media using contextual dimensions religion ideology and hate"
    - "k bert enabling language representation with knowledge graph"
    - "learning from past mistakes improving automatic speech recognition output via noisy clean phrase context modeling"
    - "ai reasoning systems pac and applied methods"

Connected through hyperedge HE-3 (induced by source paper "coherent comment generation for chinese articles with a graph to sequence model"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "sql to text generation with graph to sequence model"
    - "graph convolutional neural networks for web scale recommender systems"
    - "multiresolution graph attention networks for relevance matching"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "graph to sequence learning using gated graph neural networks"
    - "graph convolutional networks for text classification"

Connected through hyperedge HE-4 (induced by source paper "leveraging semantics for incremental learning in multi relational embeddings"):
  with members:
    - "robocse robot common sense embedding"
    - "continual lifelong learning with neural networks a review"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "continuous learning in single incremental task scenarios"
    - "deep generative dual memory network for continual learning"
    - "memory efficient experience replay for streaming learning"
    - "progressive neural networks"
    - "continual learning with deep generative replay"

Connected through hyperedge HE-5 (induced by source paper "text level graph neural network for text classification"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "graph convolutional networks for text classification"
    - "relational inductive biases deep learning and graph networks"
    - "sentence state lstm for text representation"
    - "neural message passing for quantum chemistry"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "learning combinatorial optimization algorithms over graphs"

Connected through hyperedge HE-6 (induced by source paper "graph convolution networks for probabilistic modeling of driving acceleration"):
  with members:
    - "graph neural networks for modelling traffic participant interaction"
    - "graph neural networks a review of methods and applications"
    - "an lstm network for highway trajectory prediction"
    - "probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network"
    - "semi supervised classification with graph convolutional networks"
    - "tensorflow a system for large scale machine learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "multi stage self supervised learning for graph convolutional networks"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "graph networks as learnable physics engines for inference and control"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "max margin nonparametric latent feature models for link prediction"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-8 (induced by source paper "can netgan be improved by short random walks originated from dense vertices"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "graph networks as learnable physics engines for inference and control"
    - "began boundary equilibrium generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "learning combinatorial optimization algorithms over graphs"
    - "nips 2016 tutorial generative adversarial networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"

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)