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: "aspem embedding learning by aspects in heterogeneous information networks"
  Abstract: Heterogeneous information networks (HINs) are ubiquitous in real-world applications. Due to the heterogeneity in HINs, the typed edges may not fully align with each other. In order to capture the semantic subtlety, we propose the concept of aspects with each aspect being a unit representing one underlying semantic facet. Meanwhile, network embedding has emerged as a powerful method for learning network representation, where the learned embedding can be used as features in various downstream applications. Therefore, we are motivated to propose a novel embedding learning framework---AspEm---to preserve the semantic information in HINs based on multiple aspects. Instead of preserving information of the network in one semantic space, AspEm encapsulates information regarding each aspect individually. In order to select aspects for embedding purpose, we further devise a solution for AspEm based on dataset-wide statistics. To corroborate the efficacy of AspEm, we conducted experiments on two real-words datasets with two types of applications---classification and link prediction. Experiment results demonstrate that AspEm can outperform baseline network embedding learning methods by considering multiple aspects, where the aspects can be selected from the given HIN in an unsupervised manner.

Connected through hyperedge HE-1 (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"
    - "easing embedding learning by comprehensive transcription of 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-2 (induced by source paper "hahe hierarchical attentive heterogeneous information network embedding"):
  with members:
    - "metagraph2vec complex semantic path augmented heterogeneous network embedding"
    - "hierarchical graph representation learning with differentiable pooling"
    - "attention models in graphs a survey"
    - "deeply learning molecular structure property relationships using attention and gate augmented graph convolutional network"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "heterogeneous information network embedding for recommendation"
    - "attributed network embedding for learning in a dynamic environment"
    - "heterogeneous information network embedding for meta path based proximity"

Connected through hyperedge HE-3 (induced by source paper "motif2vec motif aware node representation learning for heterogeneous networks"):
  with members:
    - "metagraph2vec complex semantic path augmented heterogeneous network embedding"
    - "graph embedding techniques applications and performance a survey"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "harp hierarchical representation learning for networks"
    - "motif based convolutional neural network on graphs"
    - "higher order organization of complex networks"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-4 (induced by source paper "similarity modeling on heterogeneous networks via automatic path discovery"):
  with members:
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "prep path based relevance from a probabilistic perspective in heterogeneous information networks"
    - "learning combinatorial optimization algorithms over graphs"
    - "heterogeneous information network embedding for meta path based proximity"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "neural combinatorial optimization with reinforcement learning"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "latent network summarization bridging network embedding and summarization"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "regal representation learning based graph alignment"
    - "graph embedding techniques applications and performance a survey"
    - "struc2vec learning node representations from structural identity"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "graph summarization methods and applications a survey"
    - "a spectral framework for anomalous subgraph detection"

Connected through hyperedge HE-6 (induced by source paper "meta graph based hin spectral embedding methods analyses and insights"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"
    - "prep path based relevance from a probabilistic perspective in heterogeneous information networks"
    - "heterogeneous information network embedding for meta path based proximity"
    - "multi view graph embedding with hub detection for brain network analysis"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "higher order organization of complex networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-7 (induced by source paper "activehne active heterogeneous network embedding"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "local spectral graph convolution for point set feature learning"
    - "graph embedding techniques applications and performance a survey"
    - "active learning for graph embedding"
    - "active discriminative text representation learning"
    - "a survey of heterogeneous information network analysis"

Connected through hyperedge HE-8 (induced by source paper "bhin2vec balancing the type of relation in heterogeneous information network"):
  with members:
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "collaborative memory network for recommendation systems"
    - "neural collaborative filtering"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "distributed representations of words and phrases and their compositionality"

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)