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: "sne signed network embedding"
  Abstract: Several network embedding models have been developed for unsigned networks. However, these models based on skip-gram cannot be applied to signed networks because they can only deal with one type of link. In this paper, we present our signed network embedding model called SNE. Our SNE adopts the log-bilinear model, uses node representations of all nodes along a given path, and further incorporates two signed-type vectors to capture the positive or negative relationship of each edge along the path. We conduct two experiments, node classification and link prediction, on both directed and undirected signed networks and compare with four baselines including a matrix factorization method and three state-of-the-art unsigned network embedding models. The experimental results demonstrate the effectiveness of our signed network embedding.

Connected through hyperedge HE-1 (induced by source paper "scalable graph embeddings via sparse transpose proximities"):
  with members:
    - "fast randomized pca for sparse data"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "verse versatile graph embeddings from similarity measures"
    - "graph embedding techniques applications and performance a survey"
    - "network representation learning a survey"
    - "struc2vec learning node representations from structural identity"
    - "a survey on network embedding"
    - "fast warped graph embedding unifying framework and one click algorithm"

Connected through hyperedge HE-2 (induced by source paper "spectral clustering of signed graphs via matrix power means"):
  with members:
    - "balance in signed networks"
    - "an mbo scheme for clustering and semi supervised clustering of signed networks"
    - "node classification for signed networks using diffuse interface methods"
    - "signed graph convolutional network"
    - "on spectral partitioning of signed graphs"
    - "a modularity based spectral method for simultaneous community and anti community detection"
    - "spectral theory of unsigned and signed graphs applications to graph clustering a survey"
    - "on the troll trust model for edge sign prediction in social networks"

Connected through hyperedge HE-3 (induced by source paper "hyperbolic node embedding for signed networks"):
  with members:
    - "poincar e glove hyperbolic word embeddings"
    - "graph embedding techniques applications and performance a survey"
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "node2vec scalable feature learning for networks"
    - "a survey of signed network mining in social media"
    - "deepwalk online learning of social representations"
    - "exploiting social network structure for person to person sentiment analysis"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "balance in signed bipartite networks"):
  with members:
    - "butterfly counting in bipartite networks"
    - "signed network modeling based on structural balance theory"
    - "measuring and modeling bipartite graphs with community structure"
    - "brazilian congress structural balance analysis"
    - "a survey of signed network mining in social media"
    - "common neighbours and the local community paradigm for topological link prediction in bipartite networks"
    - "exploiting the structure of bipartite graphs for algebraic and spectral graph theory applications"
    - "the link prediction problem in bipartite networks"

Connected through hyperedge HE-5 (induced by source paper "deep hashing for signed social network embedding"):
  with members:
    - "representation learning on graphs methods and applications"
    - "deep supervised hashing with triplet labels"
    - "node2vec scalable feature learning for networks"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "cyclical learning rates for training neural networks"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-6 (induced by source paper "signed graph attention networks"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "signed graph convolutional network"
    - "signed graph analysis for the interpretation of voting behavior"
    - "neural message passing for quantum chemistry"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-7 (induced by source paper "a tutorial on network embeddings"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "fast warped graph embedding unifying framework and one click algorithm"
    - "label noise reduction in entity typing by heterogeneous partial label embedding"
    - "node2vec scalable feature learning for networks"
    - "a general framework for content enhanced network representation learning"
    - "a neural network approach to joint modeling social networks and mobile trajectories"
    - "semi supervised classification with graph convolutional networks"
    - "don t walk skip online learning of multi scale network embeddings"

Connected through hyperedge HE-8 (induced by source paper "node classification for signed networks using diffuse interface methods"):
  with members:
    - "spectral clustering of signed graphs via matrix power means"
    - "an mbo scheme for clustering and semi supervised clustering of signed networks"
    - "community detection in networks via nonlinear modularity eigenvectors"
    - "on spectral partitioning of signed graphs"
    - "spectral theory of unsigned and signed graphs applications to graph clustering a survey"
    - "on the troll trust model for edge sign prediction in social networks"
    - "a survey of signed network mining in social media"
    - "vews a wikipedia vandal early warning system"

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)