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: "from node embedding to community embedding"
  Abstract: Most of the existing graph embedding methods focus on nodes, which aim to output a vector representation for each node in the graph such that two nodes being "close" on the graph are close too in the low-dimensional space. Despite the success of embedding individual nodes for graph analytics, we notice that an important concept of embedding communities (i.e., groups of nodes) is missing. Embedding communities is useful, not only for supporting various community-level applications, but also to help preserve community structure in graph embedding. In fact, we see community embedding as providing a higher-order proximity to define the node closeness, whereas most of the popular graph embedding methods focus on first-order and/or second-order proximities. To learn the community embedding, we hinge upon the insight that community embedding and node embedding reinforce with each other. As a result, we propose ComEmbed, the first community embedding method, which jointly optimizes the community embedding and node embedding together. We evaluate ComEmbed on real-world data sets. We show it outperforms the state-of-the-art baselines in both tasks of node classification and community prediction.

Connected through hyperedge HE-1 (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-2 (induced by source paper "can adversarial network attack be defended"):
  with members:
    - "adversarial attacks and defences competition"
    - "adversarial attack on graph structured data"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "target defense against link prediction based attacks via evolutionary perturbations"
    - "hiding individuals and communities in a social network"
    - "practical black box attacks against machine learning"
    - "node2vec scalable feature learning for 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"
    - "learning edge representations via low rank asymmetric projections"
    - "watch your step learning node embeddings via graph attention"

Connected through hyperedge HE-4 (induced by source paper "learning graph structured data using poincar e embeddings and riemannian k means algorithms"):
  with members:
    - "metric learning on manifolds"
    - "a survey on network embedding"
    - "graph2vec learning distributed representations of graphs"
    - "community enhanced network representation learning for network analysis"
    - "deepwalk online learning of social representations"
    - "distributed representations of words and phrases and their compositionality"
    - "sustaining the internet with hyperbolic mapping"

Connected through hyperedge HE-5 (induced by source paper "a comprehensive survey of graph embedding problems techniques and applications"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "struc2vec learning node representations from structural identity"
    - "geometric deep learning going beyond euclidean data"
    - "graph based isometry invariant representation learning"
    - "topological recurrent neural network for diffusion prediction"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"
    - "on the representation and embedding of knowledge bases beyond binary relations"
    - "label noise reduction in entity typing by heterogeneous partial label embedding"

Connected through hyperedge HE-6 (induced by source paper "rum network representation learning through multi level structural information preservation"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "attributed social network embedding"
    - "struc2vec learning node representations from structural identity"
    - "fast warped graph embedding unifying framework and one click algorithm"
    - "semi supervised embedding in attributed networks with outliers"
    - "node2vec scalable feature learning for networks"
    - "walklets multiscale graph embeddings for interpretable network classification"
    - "pte predictive text embedding through large scale heterogeneous text networks"

Connected through hyperedge HE-7 (induced by source paper "fast warped graph embedding unifying framework and one click algorithm"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "visualizing large scale and high dimensional data"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "supervised random walks predicting and recommending links in social networks"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-8 (induced by source paper "an automorphic distance metric and its application to node embedding for role mining"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "node2vec scalable feature learning for networks"
    - "role discovery in networks"
    - "deepwalk online learning of social representations"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "representation learning a review and new perspectives"
    - "axiomatic ranking of network role similarity"

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)