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: "semi supervised embedding in attributed networks with outliers"
  Abstract: In this paper, we propose a novel framework, called Semi-supervised Embedding in Attributed Networks with Outliers (SEANO), to learn a low-dimensional vector representation that systematically captures the topological proximity, attribute affinity and label similarity of vertices in a partially labeled attributed network (PLAN). Our method is designed to work in both transductive and inductive settings while explicitly alleviating noise effects from outliers. Experimental results on various datasets drawn from the web, text and image domains demonstrate the advantages of SEANO over state-of-the-art methods in semi-supervised classification under transductive as well as inductive settings. We also show that a subset of parameters in SEANO is interpretable as outlier score and can significantly outperform baseline methods when applied for detecting network outliers. Finally, we present the use of SEANO in a challenging real-world setting -- flood mapping of satellite images and show that it is able to outperform modern remote sensing algorithms for this task.

Connected through hyperedge HE-1 (induced by source paper "network representation learning consolidation and renewed bearing"):
  with members:
    - "netsmf large scale network embedding as sparse matrix factorization"
    - "pl nmf parallel locality optimized non negative matrix factorization"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "mile a multi level framework for scalable graph embedding"
    - "verse versatile graph embeddings from similarity measures"
    - "fusion graph convolutional networks"
    - "systematic biases in link prediction comparing heuristic and graph embedding based methods"
    - "graph embedding techniques applications and performance a survey"

Connected through hyperedge HE-2 (induced by source paper "surfcon synonym discovery on privacy aware clinical data"):
  with members:
    - "synonymnet multi context bilateral matching for entity synonyms"
    - "mining entity synonyms with efficient neural set generation"
    - "automatic synonym discovery with knowledge bases"
    - "attention is all you need"
    - "enriching word vectors with subword information"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "charagram embedding words and sentences via character n grams"
    - "revisiting semi supervised learning with graph embeddings"

Connected through hyperedge HE-3 (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"
    - "learning edge representations via low rank asymmetric projections"
    - "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-4 (induced by source paper "network embedding for cross network node classification"):
  with members:
    - "deep network embedding for graph representation learning in signed networks"
    - "struc2vec learning node representations from structural identity"
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"
    - "transfer learning across networks for collective classification"
    - "a kernel method for the two sample problem"

Connected through hyperedge HE-5 (induced by source paper "imverde vertex diminished random walk for learning network representation from imbalanced data"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "struc2vec learning node representations from structural identity"
    - "representation learning on graphs methods and applications"
    - "a survey on network embedding"
    - "learning graph representations with embedding propagation"
    - "node2vec scalable feature learning for networks"
    - "revisiting semi supervised learning with graph embeddings"

Connected through hyperedge HE-6 (induced by source paper "outlier aware network embedding for attributed networks"):
  with members:
    - "struc2vec learning node representations from structural identity"
    - "representation learning on graphs methods and applications"
    - "word translation without parallel data"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "unsupervised deep embedding for clustering analysis"
    - "line large scale information network embedding"

Connected through hyperedge HE-7 (induced by source paper "mile a multi level framework for scalable graph embedding"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "harp hierarchical representation learning for networks"
    - "principled multilayer network embedding"
    - "node2vec scalable feature learning for networks"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "deep convolutional networks on graph structured data"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-8 (induced by source paper "coldroute effective routing of cold questions in stack exchange sites"):
  with members:
    - "faster way to agony discovering hierarchies in directed graphs"
    - "qdee question difficulty and expertise estimation in community question answering sites"
    - "node2vec scalable feature learning for networks"
    - "hierarchies in directed networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "distributed representations of sentences and documents"
    - "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)