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: "rotate knowledge graph embedding by relational rotation in complex space"
  Abstract: We study the problem of learning representations of entities and relations in knowledge graphs for predicting missing links. The success of such a task heavily relies on the ability of modeling and inferring the patterns of (or between) the relations. In this paper, we present a new approach for knowledge graph embedding called RotatE, which is able to model and infer various relation patterns including: symmetry/antisymmetry, inversion, and composition. Specifically, the RotatE model defines each relation as a rotation from the source entity to the target entity in the complex vector space. In addition, we propose a novel self-adversarial negative sampling technique for efficiently and effectively training the RotatE model. Experimental results on multiple benchmark knowledge graphs show that the proposed RotatE model is not only scalable, but also able to infer and model various relation patterns and significantly outperform existing state-of-the-art models for link prediction.

Connected through hyperedge HE-1 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs"):
  with members:
    - "a survey on knowledge graphs representation acquisition and applications"
    - "knowledge graph embedding for link prediction a comparative analysis"
    - "multi view knowledge graph embedding for entity alignment"
    - "cross lingual knowledge graph alignment via graph matching neural network"
    - "jointly learning entity and relation representations for entity alignment"
    - "analyzing the limitations of cross lingual word embedding mappings"
    - "learning to exploit long term relational dependencies in knowledge graphs"
    - "aligning cross lingual entities with multi aspect information"

Connected through hyperedge HE-2 (induced by source paper "knowledge graph embedding for link prediction a comparative analysis"):
  with members:
    - "analyzing knowledge graph embedding methods from a multi embedding interaction perspective"
    - "learning to exploit long term relational dependencies in knowledge graphs"
    - "tucker tensor factorization for knowledge graph completion"
    - "interaction embeddings for prediction and explanation in knowledge graphs"
    - "a capsule network based embedding model for knowledge graph completion and search personalization"
    - "on evaluating embedding models for knowledge base completion"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "toruse knowledge graph embedding on a lie group"

Connected through hyperedge HE-3 (induced by source paper "entity context and relational paths for knowledge graph completion"):
  with members:
    - "multi task feature learning for knowledge graph enhanced recommendation"
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "drum end to end differentiable rule mining on knowledge graphs"
    - "coke contextualized knowledge graph embedding"
    - "iteratively learning embeddings and rules for knowledge graph reasoning"
    - "position aware graph neural networks"
    - "gnnexplainer generating explanations for graph neural networks"
    - "knowledge graph convolutional networks for recommender systems"

Connected through hyperedge HE-4 (induced by source paper "magnn metapath aggregated graph neural network for heterogeneous graph embedding"):
  with members:
    - "star gcn stacked and reconstructed graph convolutional networks for recommender systems"
    - "position aware graph neural networks"
    - "knowledge graph convolutional networks for recommender systems"
    - "representation learning for attributed multiplex heterogeneous network"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "attention is all you need"
    - "heterogeneous information network embedding for recommendation"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"

Connected through hyperedge HE-5 (induced by source paper "efficient rule learning with template saturation for knowledge graph completion"):
  with members:
    - "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"
    - "graph neural networks a review of methods and applications"

Connected through hyperedge HE-6 (induced by source paper "inductive relation prediction by subgraph reasoning"):
  with members:
    - "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"
    - "representation learning on graphs methods and applications"

Connected through hyperedge HE-7 (induced by source paper "query2box reasoning over knowledge graphs in vector space using box embeddings"):
  with members:
    - "on understanding knowledge graph representation"
    - "deep sets"
    - "embedding logical queries on knowledge graphs"
    - "hierarchical density order embeddings"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "improved representation learning for predicting commonsense ontologies"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "traversing knowledge graphs in vector space"

Connected through hyperedge HE-8 (induced by source paper "open knowledge enrichment for long tail entities"):
  with members:
    - "long tail relation extraction via knowledge graph embeddings and graph convolution networks"
    - "truth inference at scale a bayesian model for adjudicating highly redundant crowd annotations"
    - "ceres distantly supervised relation extraction from the semi structured web"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "open world knowledge graph completion"
    - "convolutional 2d knowledge graph embeddings"
    - "document filtering for long tail entities"

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.LG(Machine Learning)