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: "learning edge representations via low rank asymmetric projections"
  Abstract: We propose a new method for embedding graphs while preserving directed edge information. Learning such continuous-space vector representations (or embeddings) of nodes in a graph is an important first step for using network information (from social networks, user-item graphs, knowledge bases, etc.) in many machine learning tasks. Unlike previous work, we (1) explicitly model an edge as a function of node embeddings, and we (2) propose a novel objective, the graph likelihood, which contrasts information from sampled random walks with non-existent edges. Individually, both of these contributions improve the learned representations, especially when there are memory constraints on the total size of the embeddings. When combined, our contributions enable us to significantly improve the state-of-the-art by learning more concise representations that better preserve the graph structure. We evaluate our method on a variety of link-prediction task including social networks, collaboration networks, and protein interactions, showing that our proposed method learn representations with error reductions of up to 76% and 55%, on directed and undirected graphs. In addition, we show that the representations learned by our method are quite space efficient, producing embeddings which have higher structure-preserving accuracy but are 10 times smaller.

Connected through hyperedge HE-1 (induced by source paper "heterogeneous graph learning for visual commonsense reasoning"):
  with members:
    - "image question answer synergistic network for visual dialog"
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "bag bi directional attention entity graph convolutional network for multi hop reasoning question answering"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "information maximizing visual question generation"
    - "from recognition to cognition visual commonsense reasoning"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "learning to compose dynamic tree structures for visual contexts"

Connected through hyperedge HE-2 (induced by source paper "ddgk learning graph representations for deep divergence graph kernels"):
  with members:
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "netlsd hearing the shape of a graph"
    - "hierarchical graph representation learning with differentiable pooling"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "a tutorial on network embeddings"
    - "graph edit distance computation via graph neural networks"
    - "verse versatile graph embeddings from similarity measures"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-3 (induced by source paper "is a single embedding enough learning node representations that capture multiple social contexts"):
  with members:
    - "ddgk learning graph representations for deep divergence graph kernels"
    - "enhanced network embeddings via exploiting edge labels"
    - "a tutorial on network embeddings"
    - "verse versatile graph embeddings from similarity measures"
    - "struc2vec learning node representations from structural identity"
    - "harp hierarchical representation learning for networks"
    - "watch your step learning node embeddings via graph attention"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-4 (induced by source paper "micro and macro level churn analysis of large scale mobile games"):
  with members:
    - "a semi supervised and inductive embedding model for churn prediction of large scale mobile games"
    - "streaming network embedding through local actions"
    - "semi supervised embedding in attributed networks with outliers"
    - "customer lifetime value prediction using embeddings"
    - "node2vec scalable feature learning for networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "beyond node embedding a direct unsupervised edge representation framework for homogeneous networks"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "dual primal graph convolutional networks"
    - "outlier aware network embedding for attributed networks"
    - "fscnmf fusing structure and content via non negative matrix factorization for embedding information networks"
    - "struc2vec learning node representations from structural identity"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "easing embedding learning by comprehensive transcription of heterogeneous information networks"):
  with members:
    - "aspem embedding learning by aspects in heterogeneous information networks"
    - "struc2vec learning node representations from structural identity"
    - "representation learning on graphs methods and applications"
    - "prep path based relevance from a probabilistic perspective in heterogeneous information networks"
    - "name disambiguation in anonymized graphs using network embedding"
    - "poincar e embeddings for learning hierarchical representations"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-7 (induced by source paper "node representation learning for directed graphs"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "verse versatile graph embeddings from similarity measures"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-8 (induced by source paper "enhanced network embeddings via exploiting edge labels"):
  with members:
    - "sne signed network embedding"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "image based recommendations on styles and substitutes"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"

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)