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: "iteratively learning embeddings and rules for knowledge graph reasoning"
  Abstract: Reasoning is essential for the development of large knowledge graphs, especially for completion, which aims to infer new triples based on existing ones. Both rules and embeddings can be used for knowledge graph reasoning and they have their own advantages and difficulties. Rule-based reasoning is accurate and explainable but rule learning with searching over the graph always suffers from efficiency due to huge search space. Embedding-based reasoning is more scalable and efficient as the reasoning is conducted via computation between embeddings, but it has difficulty learning good representations for sparse entities because a good embedding relies heavily on data richness. Based on this observation, in this paper we explore how embedding and rule learning can be combined together and complement each other's difficulties with their advantages. We propose a novel framework IterE iteratively learning embeddings and rules, in which rules are learned from embeddings with proper pruning strategy and embeddings are learned from existing triples and new triples inferred by rules. Evaluations on embedding qualities of IterE show that rules help improve the quality of sparse entity embeddings and their link prediction results. We also evaluate the efficiency of rule learning and quality of rules from IterE compared with AMIE+, showing that IterE is capable of generating high quality rules more efficiently. Experiments show that iteratively learning embeddings and rules benefit each other during learning and prediction.

Connected through hyperedge HE-1 (induced by source paper "entity context and relational paths for knowledge graph completion"):
  with members:
    - "multi task feature learning for knowledge graph enhanced recommendation"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "drum end to end differentiable rule mining on knowledge graphs"
    - "coke contextualized knowledge graph embedding"
    - "position aware graph neural networks"
    - "gnnexplainer generating explanations for graph neural networks"
    - "knowledge graph convolutional networks for recommender systems"

Connected through hyperedge HE-2 (induced by source paper "relation adversarial network for low resource knowledgegraph completion"):
  with members:
    - "long tail relation extraction via knowledge graph embeddings and graph convolution networks"
    - "multi task feature learning for knowledge graph enhanced recommendation"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "kg bert bert for knowledge graph completion"
    - "ernie enhanced language representation with informative entities"
    - "transfer learning for relation extraction via relation gated adversarial learning"
    - "meta relational learning for few shot link prediction in knowledge graphs"
    - "adversarial domain adaptation for duplicate question detection"

Connected through hyperedge HE-3 (induced by source paper "meta relational learning for few shot link prediction in knowledge graphs"):
  with members:
    - "long tail relation extraction via knowledge graph embeddings and graph convolution networks"
    - "interaction embeddings for prediction and explanation in knowledge graphs"
    - "one shot relational learning for knowledge graphs"
    - "prototypical networks for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "convolutional 2d knowledge graph embeddings"
    - "matching networks for one shot learning"
    - "complex embeddings for simple link prediction"

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)