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: "end to end structure aware convolutional networks for knowledge base completion"
  Abstract: Knowledge graph embedding has been an active research topic for knowledge base completion, with progressive improvement from the initial TransE, TransH, DistMult et al to the current state-of-the-art ConvE. ConvE uses 2D convolution over embeddings and multiple layers of nonlinear features to model knowledge graphs. The model can be efficiently trained and scalable to large knowledge graphs. However, there is no structure enforcement in the embedding space of ConvE. The recent graph convolutional network (GCN) provides another way of learning graph node embedding by successfully utilizing graph connectivity structure. In this work, we propose a novel end-to-end Structure-Aware Convolutional Network (SACN) that takes the benefit of GCN and ConvE together. SACN consists of an encoder of a weighted graph convolutional network (WGCN), and a decoder of a convolutional network called Conv-TransE. WGCN utilizes knowledge graph node structure, node attributes and edge relation types. It has learnable weights that adapt the amount of information from neighbors used in local aggregation, leading to more accurate embeddings of graph nodes. Node attributes in the graph are represented as additional nodes in the WGCN. The decoder Conv-TransE enables the state-of-the-art ConvE to be translational between entities and relations while keeps the same link prediction performance as ConvE. We demonstrate the effectiveness of the proposed SACN on standard FB15k-237 and WN18RR datasets, and it gives about 10% relative improvement over the state-of-the-art ConvE in terms of HITS@1, HITS@3 and HITS@10.

Connected through hyperedge HE-1 (induced by source paper "efficient rule learning with template saturation for knowledge graph completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "drum end to end differentiable rule mining on knowledge graphs"
    - "rule guided compositional representation learning on knowledge graphs"
    - "learning attention based embeddings for relation prediction in knowledge graphs"
    - "learning hierarchy aware knowledge graph embeddings for link prediction"
    - "interacte improving convolution based knowledge graph embeddings by increasing feature interactions"
    - "tucker tensor factorization for knowledge graph completion"
    - "composition based multi relational graph convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "on the role of conceptualization in commonsense knowledge graph construction"):
  with members:
    - "kg bert bert for knowledge graph completion"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "aser a large scale eventuality knowledge graph"
    - "roberta a robustly optimized bert pretraining approach"
    - "a simple method for commonsense reasoning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"
    - "embedding entities and relations for learning and inference in knowledge bases"

Connected through hyperedge HE-3 (induced by source paper "fast evaluation of link prediction by random sampling of unobserved links"):
  with members:
    - "gcn gan a non linear temporal link prediction model for weighted dynamic networks"
    - "link prediction based on graph neural networks"
    - "link prediction in dynamic graphs for recommendation"
    - "multi task graph autoencoders"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "gemsec graph embedding with self clustering"
    - "scalable temporal latent space inference for link prediction in dynamic social networks"
    - "performance of local information based link prediction a sampling perspective"

Connected through hyperedge HE-4 (induced by source paper "composition based multi relational graph convolutional networks"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "hypernetwork knowledge graph embeddings"
    - "fast graph representation learning with pytorch geometric"
    - "dual primal graph convolutional networks"
    - "graph to sequence learning using gated graph neural networks"
    - "how powerful are graph neural networks"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "kbgan adversarial learning for knowledge graph embeddings"

Connected through hyperedge HE-5 (induced by source paper "selective attention based graph convolutional networks for aspect level sentiment classification"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "targeted sentiment classification with attentional encoder network"
    - "densely connected graph convolutional networks for graph to sequence learning"
    - "multi hop reading comprehension across multiple documents by reasoning over heterogeneous graphs"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "attention guided graph convolutional networks for relation extraction"
    - "modeling sentiment dependencies with graph convolutional networks for aspect level sentiment classification"
    - "exploiting coarse to fine task transfer for aspect level sentiment classification"

Connected through hyperedge HE-6 (induced by source paper "interacte improving convolution based knowledge graph embeddings by increasing feature interactions"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "a re evaluation of knowledge graph completion methods"
    - "cesi canonicalizing open knowledge bases using embeddings and side information"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "omnidirectional cnn for visual place recognition and navigation"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "kblrn end to end learning of knowledge base representations with latent relational and numerical features"
    - "kbgan adversarial learning for knowledge graph embeddings"

Connected through hyperedge HE-7 (induced by source paper "orthogonal relation transforms with graph context modeling for knowledge graph embedding"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "learning attention based embeddings for relation prediction in knowledge graphs"
    - "tucker tensor factorization for knowledge graph completion"
    - "fast graph representation learning with pytorch geometric"
    - "a capsule network based embedding model for knowledge graph completion and search personalization"
    - "can we gain more from orthogonality regularizations in training deep cnns"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "orthogonal weight normalization solution to optimization over multiple dependent stiefel manifolds in deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "commonsense knowledge base completion with structural and semantic context"):
  with members:
    - "commonsense reasoning for natural language understanding a survey of benchmarks resources and approaches"
    - "language models as knowledge bases"
    - "probing the need for visual context in multimodal machine translation"
    - "commonsense knowledge mining from pretrained models"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "answering science exam questions using query rewriting with background knowledge"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "commonsense mining as knowledge base completion a study on the impact of novelty"

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)