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 representation learning a quantitative review"
  Abstract: Knowledge representation learning (KRL) aims to represent entities and relations in knowledge graph in low-dimensional semantic space, which have been widely used in massive knowledge-driven tasks. In this article, we introduce the reader to the motivations for KRL, and overview existing approaches for KRL. Afterwards, we extensively conduct and quantitative comparison and analysis of several typical KRL methods on three evaluation tasks of knowledge acquisition including knowledge graph completion, triple classification, and relation extraction. We also review the real-world applications of KRL, such as language modeling, question answering, information retrieval, and recommender systems. Finally, we discuss the remaining challenges and outlook the future directions for KRL. The codes and datasets used in the experiments can be found in this https URL.

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"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "autokge searching scoring functions for knowledge graph embedding"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "tucker tensor factorization for knowledge graph completion"
    - "searching for interaction functions in collaborative filtering"
    - "nscaching simple and efficient negative sampling for knowledge graph embedding"
    - "taking human out of learning applications a survey on automated machine learning"
    - "neural architecture optimization"
    - "darts differentiable architecture search"
    - "on evaluating embedding models for knowledge base completion"

Connected through hyperedge HE-3 (induced by source paper "knowledge graph alignment network with gated multi hop neighborhood aggregation"):
  with members:
    - "multi view knowledge graph embedding for entity alignment"
    - "cross lingual knowledge graph alignment via graph matching neural network"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "learning to exploit long term relational dependencies in knowledge graphs"
    - "multi channel graph neural network for entity alignment"
    - "relation aware entity alignment for heterogeneous knowledge graphs"
    - "graph matching networks for learning the similarity of graph structured objects"

Connected through hyperedge HE-4 (induced by source paper "acekg a large scale knowledge graph for academic data mining"):
  with members:
    - "pte predictive text embedding through large scale heterogeneous text networks"
    - "line large scale information network embedding"
    - "holographic embeddings of knowledge graphs"
    - "deepwalk online learning of social representations"

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.CL(Computation and Language)