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: "modeling large scale structured relationships with shared memory for knowledge base completion"
  Abstract: Recent studies on knowledge base completion, the task of recovering missing relationships based on recorded relations, demonstrate the importance of learning embeddings from multi-step relations. However, due to the size of knowledge bases, learning multi-step relations directly on top of observed triplets could be costly. Hence, a manually designed procedure is often used when training the models. In this paper, we propose Implicit ReasoNets (IRNs), which is designed to perform multi-step inference implicitly through a controller and shared memory. Without a human-designed inference procedure, IRNs use training data to learn to perform multi-step inference in an embedding neural space through the shared memory and controller. While the inference procedure does not explicitly operate on top of observed triplets, our proposed model outperforms all previous approaches on the popular FB15k benchmark by more than 5.7%.

Connected through hyperedge HE-1 (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-2 (induced by source paper "iteratively learning embeddings and rules for knowledge graph reasoning"):
  with members:
    - "improving knowledge graph embedding using simple constraints"
    - "from knowledge graph embedding to ontology embedding an analysis of the compatibility between vector space representations and rules"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "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"
    - "kbqa learning question answering over qa corpora and knowledge bases"
    - "analogical inference for multi relational embeddings"

Connected through hyperedge HE-3 (induced by source paper "reinforcewalk learning to walk in graph with monte carlo tree search"):
  with members:
    - "neural approaches to conversational ai"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "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"
    - "convolutional 2d knowledge graph embeddings"
    - "learning to reinforcement learn"
    - "seqgan sequence generative adversarial nets with policy gradient"

Connected through hyperedge HE-4 (induced by source paper "interpretable and compositional relation learning by joint training with an autoencoder"):
  with members:
    - "an interpretable knowledge transfer model for knowledge base completion"
    - "knowledge base completion baselines strike back"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "learning semantically and additively compositional distributional representations"
    - "proje embedding projection for knowledge graph completion"
    - "from one point to a manifold knowledge graph embedding for precise link prediction"
    - "traversing knowledge graphs in vector space"
    - "compositional vector space models for knowledge base completion"

Connected through hyperedge HE-5 (induced by source paper "an overview of embedding models of entities and relationships for knowledge base completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "hypernetwork knowledge graph embeddings"
    - "a capsule network based embedding model for knowledge graph completion and search personalization"
    - "recurrent one hop predictions for reasoning over knowledge graphs"
    - "variational knowledge graph reasoning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "interpretable and compositional relation learning by joint training with an autoencoder"
    - "end to end structure aware convolutional networks for knowledge base completion"

Connected through hyperedge HE-6 (induced by source paper "starspace embed all the things"):
  with members:
    - "supervised learning of universal sentence representations from natural language inference data"
    - "knowledge base completion baselines strike back"
    - "very deep convolutional networks for text classification"
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"
    - "efficient character level document classification by combining convolution and recurrent layers"
    - "text understanding from scratch"
    - "connecting language and knowledge bases with embedding models for relation extraction"

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)