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: "easing embedding learning by comprehensive transcription of heterogeneous information networks"
  Abstract: Heterogeneous information networks (HINs) are ubiquitous in real-world applications. In the meantime, network embedding has emerged as a convenient tool to mine and learn from networked data. As a result, it is of interest to develop HIN embedding methods. However, the heterogeneity in HINs introduces not only rich information but also potentially incompatible semantics, which poses special challenges to embedding learning in HINs. With the intention to preserve the rich yet potentially incompatible information in HIN embedding, we propose to study the problem of comprehensive transcription of heterogeneous information networks. The comprehensive transcription of HINs also provides an easy-to-use approach to unleash the power of HINs, since it requires no additional supervision, expertise, or feature engineering. To cope with the challenges in the comprehensive transcription of HINs, we propose the HEER algorithm, which embeds HINs via edge representations that are further coupled with properly-learned heterogeneous metrics. To corroborate the efficacy of HEER, we conducted experiments on two large-scale real-words datasets with an edge reconstruction task and multiple case studies. Experiment results demonstrate the effectiveness of the proposed HEER model and the utility of edge representations and heterogeneous metrics. The code and data are available at https://github.com/GentleZhu/HEER.

Connected through hyperedge HE-1 (induced by source paper "discovering hypernymy in text rich heterogeneous information network by exploiting context granularity"):
  with members:
    - "similarity modeling on heterogeneous networks via automatic path discovery"
    - "meta graph based hin spectral embedding methods analyses and insights"
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "hiexpan task guided taxonomy construction by hierarchical tree expansion"
    - "scoring lexical entailment with a supervised directional similarity network"
    - "entity set search of scientific literature an unsupervised ranking approach"
    - "term definitions help hypernymy detection"
    - "taxogen unsupervised topic taxonomy construction by adaptive term embedding and clustering"

Connected through hyperedge HE-2 (induced by source paper "task guided pair embedding in heterogeneous network"):
  with members:
    - "link prediction based on graph neural networks"
    - "metagraph2vec complex semantic path augmented heterogeneous network embedding"
    - "aspem embedding learning by aspects in heterogeneous information networks"
    - "capturing edge attributes via network embedding"
    - "an attention based collaboration framework for multi view network representation learning"
    - "advances in pre training distributed word representations"
    - "heterogeneous information network embedding for recommendation"
    - "effectiveness of anonymization in double blind review"

Connected through hyperedge HE-3 (induced by source paper "heterogeneous graph attention network"):
  with members:
    - "embedding logical queries on knowledge graphs"
    - "graph embedding techniques applications and performance a survey"
    - "joint embedding of meta path and meta graph for heterogeneous information networks"
    - "attention is all you need"
    - "a survey on network embedding"
    - "heterogeneous information network embedding for recommendation"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-4 (induced by source paper "jscn joint spectral convolutional network for cross domain recommendation"):
  with members:
    - "deep cross networks with aesthetic preference for cross domain recommendation"
    - "deep learning based recommender system a survey and new perspectives"
    - "conet collaborative cross networks for cross domain recommendation"
    - "spectral collaborative filtering"
    - "neural collaborative filtering"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "cross stitch networks for multi task learning"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"

Connected through hyperedge HE-5 (induced by source paper "is a single vector enough exploring node polysemy for network embedding"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "techniques for interpretable machine learning"
    - "taxogen unsupervised topic taxonomy construction by adaptive term embedding and clustering"
    - "attributed social network embedding"
    - "representation learning on graphs methods and applications"
    - "a survey on network embedding"
    - "attributed network embedding for learning in a dynamic environment"

Connected through hyperedge HE-6 (induced by source paper "hetespaceywalk a heterogeneous spacey random walk for heterogeneous information network embedding"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "metagraph2vec complex semantic path augmented heterogeneous network embedding"
    - "carl content aware representation learning for heterogeneous networks"
    - "joint embedding of meta path and meta graph for heterogeneous information networks"
    - "the spacey random walk a stochastic process for higher order data"
    - "heterogeneous information network embedding for recommendation"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"

Connected through hyperedge HE-7 (induced by source paper "temporal network embedding with micro and macro dynamics"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "dyngem deep embedding method for dynamic graphs"
    - "representation learning over dynamic graphs"
    - "a survey on network embedding"
    - "heterogeneous information network embedding for recommendation"
    - "attributed network embedding for learning in a dynamic environment"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "the neural hawkes process a neurally self modulating multivariate point process"

Connected through hyperedge HE-8 (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"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "cross language citation recommendation via hierarchical representation learning on heterogeneous graph"
    - "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"

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.SI(Social and Information Networks)