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 graph completion via complex tensor factorization"
  Abstract: In statistical relational learning, knowledge graph completion deals with automatically understanding the structure of large knowledge graphs---labeled directed graphs---and predicting missing relationships---labeled edges. State-of-the-art embedding models propose different trade-offs between modeling expressiveness, and time and space complexity. We reconcile both expressiveness and complexity through the use of complex-valued embeddings and explore the link between such complex-valued embeddings and unitary diagonalization. We corroborate our approach theoretically and show that all real square matrices---thus all possible relation/adjacency matrices---are the real part of some unitarily diagonalizable matrix. This results opens the door to a lot of other applications of square matrices factorization. Our approach based on complex embeddings is arguably simple, as it only involves a Hermitian dot product, the complex counterpart of the standard dot product between real vectors, whereas other methods resort to more and more complicated composition functions to increase their expressiveness. The proposed complex embeddings are scalable to large data sets as it remains linear in both space and time, while consistently outperforming alternative approaches on standard link prediction benchmarks.

Connected through hyperedge HE-1 (induced by source paper "inductive relation prediction by subgraph reasoning"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "drum end to end differentiable rule mining on knowledge graphs"
    - "clutrr a diagnostic benchmark for inductive reasoning from text"
    - "link prediction based on graph neural networks"
    - "logic attention based neighborhood aggregation for inductive knowledge graph embedding"
    - "how powerful are graph neural networks"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"

Connected through hyperedge HE-2 (induced by source paper "explainable deep rdfs reasoner"):
  with members:
    - "reasoning over rdf knowledge bases using deep learning"
    - "opening the black box of deep neural networks via information"
    - "deep learning for ontology reasoning"
    - "holographic embeddings of knowledge graphs"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-3 (induced by source paper "cognitive knowledge graph reasoning for one shot relational learning"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "hypernetwork knowledge graph embeddings"
    - "tucker tensor factorization for knowledge graph completion"
    - "meta learning for low resource neural machine translation"
    - "relational inductive biases deep learning and graph networks"
    - "variational knowledge graph reasoning"
    - "one shot relational learning for knowledge graphs"

Connected through hyperedge HE-4 (induced by source paper "diachronic embedding for temporal knowledge graph completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "relational representation learning for dynamic knowledge graphs a survey"
    - "hypernetwork knowledge graph embeddings"
    - "tucker tensor factorization for knowledge graph completion"
    - "improved knowledge graph embedding using background taxonomic information"
    - "how powerful are graph neural networks"
    - "learning dynamic embeddings from temporal interactions"
    - "learning sequence encoders for temporal knowledge graph completion"

Connected through hyperedge HE-5 (induced by source paper "information extraction from scientific literature for method recommendation"):
  with members:
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "graph based semi supervised conditional random fields for spoken language understanding using unaligned data"
    - "semeval 2017 task 10 scienceie extracting keyphrases and relations from scientific publications"
    - "scientific information extraction with semi supervised neural tagging"
    - "multi task learning for speaker role adaptation in neural conversation models"
    - "distant supervision for relation extraction beyond the sentence boundary"
    - "lstm based conversation models"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-6 (induced by source paper "knowledge hypergraphs prediction beyond binary relations"):
  with members:
    - "hypernetwork knowledge graph embeddings"
    - "improved knowledge graph embedding using background taxonomic information"
    - "hypergraph neural networks"
    - "hypergcn a new method of training graph convolutional networks on hypergraphs"
    - "how powerful are graph neural networks"
    - "an overview of embedding models of entities and relationships for knowledge base completion"
    - "knowledge base completion baselines strike back"
    - "convolutional 2d knowledge graph embeddings"

Connected through hyperedge HE-7 (induced by source paper "variational knowledge graph reasoning"):
  with members:
    - "generating sentences by editing prototypes"
    - "variational reasoning for question answering with knowledge graph"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "variational neural machine translation"
    - "generating images from captions with attention"

Connected through hyperedge HE-8 (induced by source paper "on inductive abilities of latent factor models for relational learning"):
  with members:
    - "knowledge base completion with out of knowledge base entities a graph neural network approach"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "complex and holographic embeddings of knowledge graphs a comparison"
    - "adversarial sets for regularising neural link predictors"
    - "generalizing to unseen entities and entity pairs with row less universal schema"
    - "basic reasoning with tensor product representations"
    - "complex embeddings for simple link prediction"
    - "towards ai complete question answering a set of prerequisite toy tasks"

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)