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: "cross language citation recommendation via hierarchical representation learning on heterogeneous graph"
  Abstract: While the volume of scholarly publications has increased at a frenetic pace, accessing and consuming the useful candidate papers, in very large digital libraries, is becoming an essential and challenging task for scholars. Unfortunately, because of language barrier, some scientists (especially the junior ones or graduate students who do not master other languages) cannot efficiently locate the publications hosted in a foreign language repository. In this study, we propose a novel solution, cross-language citation recommendation via Hierarchical Representation Learning on Heterogeneous Graph (HRLHG), to address this new problem. HRLHG can learn a representation function by mapping the publications, from multilingual repositories, to a low-dimensional joint embedding space from various kinds of vertexes and relations on a heterogeneous graph. By leveraging both global (task specific) plus local (task independent) information as well as a novel supervised hierarchical random walk algorithm, the proposed method can optimize the publication representations by maximizing the likelihood of locating the important cross-language neighborhoods on the graph. Experiment results show that the proposed method can not only outperform state-of-the-art baseline models, but also improve the interpretability of the representation model for cross-language citation recommendation task.

Connected through hyperedge HE-1 (induced by source paper "citation recommendation approaches and datasets"):
  with members:
    - "content based citation recommendation"
    - "hyperdoc2vec distributed representations of hypertext documents"
    - "core elements in the process of citing publications conceptual overview of the literature"
    - "machine learning vs rules and out of the box vs retrained an evaluation of open source bibliographic reference and citation parsers"
    - "on measuring bias in online information"
    - "all fingers are not equal intensity of references in scientific articles"
    - "finding news citations for wikipedia"
    - "automated news suggestions for populating wikipedia entity pages"

Connected through hyperedge HE-2 (induced by source paper "hybridcite a hybrid model for context aware citation recommendation"):
  with members:
    - "a scalable hybrid research paper recommender system for microsoft academic"
    - "content based citation recommendation"
    - "hyperdoc2vec distributed representations of hypertext documents"
    - "combining aspects of genetic algorithms with weighted recommender hybridization"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "cross domain aspect category transfer and detection via traceable heterogeneous graph representation learning"):
  with members:
    - "mathematics content understanding for cyberlearning via formula evolution map"
    - "attention is all you need"
    - "recent trends in deep learning based natural language processing"
    - "aspect level sentiment classification with deep memory network"
    - "node2vec scalable feature learning for networks"
    - "insight 1 at semeval 2016 task 5 deep learning for multilingual aspect based sentiment analysis"
    - "semi supervised classification with graph convolutional networks"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-4 (induced by source paper "detect camouflaged spam content via stoneskipping graph and text joint embedding for chinese character variation representation"):
  with members:
    - "mathematics content understanding for cyberlearning via formula evolution map"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning chinese word representations from glyphs of characters"
    - "attention is all you need"
    - "node2vec scalable feature learning for networks"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-5 (induced by source paper "graph embedding for citation recommendation"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "node2vec scalable feature learning for networks"
    - "graph summarization methods and applications a survey"
    - "deepwalk online learning of social representations"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"

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.IR(Information Retrieval)