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: "is a single embedding enough learning node representations that capture multiple social contexts"
  Abstract: Recent interest in graph embedding methods has focused on learning a single representation for each node in the graph. But can nodes really be best described by a single vector representation? In this work, we propose a method for learning multiple representations of the nodes in a graph (e.g., the users of a social network). Based on a principled decomposition of the ego-network, each representation encodes the role of the node in a different local community in which the nodes participate. These representations allow for improved reconstruction of the nuanced relationships that occur in the graph - a phenomenon that we illustrate through state-of-the-art results on link prediction tasks on a variety of graphs, reducing the error by up to 90%. In addition, we show that these embeddings allow for effective visual analysis of the learned community structure.

Connected through hyperedge HE-1 (induced by source paper "graph neighborhood attentive pooling"):
  with members:
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "simplifying graph convolutional networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "diffusion maps for textual network embedding"
    - "attentive pooling networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-2 (induced by source paper "influence maximization in unknown social networks learning policies for effective graph sampling"):
  with members:
    - "learning structural node embeddings via diffusion wavelets"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"
    - "prioritized experience replay"
    - "deepwalk online learning of social representations"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "irie scalable and robust influence maximization in social networks"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-3 (induced by source paper "which way direction aware attributed graph embedding"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "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"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "on the equivalence between node embeddings and structural graph representations"):
  with members:
    - "from community to role based graph embeddings"
    - "graph representation ensemble learning"
    - "on the limitations of representing functions on sets"
    - "position aware graph neural networks"
    - "what can neural networks reason about"
    - "correlated variational auto encoders"
    - "on the equivalence between graph isomorphism testing and function approximation with gnns"
    - "a comprehensive survey on graph neural networks"

Connected through hyperedge HE-5 (induced by source paper "ddgk learning graph representations for deep divergence graph kernels"):
  with members:
    - "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"
    - "on the dimensionality of word embedding"

Connected through hyperedge HE-6 (induced by source paper "span core decomposition for temporal networks algorithms and applications"):
  with members:
    - "a survey of community search over big graphs"
    - "finding events in temporal networks segmentation meets densest subgraph discovery"
    - "graph embedding techniques applications and performance a survey"
    - "on finding dense common subgraphs"
    - "listing all maximal k plexes in temporal graphs"
    - "mining maximal span cores from temporal networks"
    - "to be connected or not to be connected that is the minimum inefficiency subgraph problem"
    - "the complexity of finding small separators in temporal graphs"

Connected through hyperedge HE-7 (induced by source paper "exponential family graph embeddings"):
  with members:
    - "netsmf large scale network embedding as sparse matrix factorization"
    - "kernel node embeddings"
    - "a comprehensive survey on graph neural networks"
    - "tne a latent model for representation learning on networks"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "biasedwalk biased sampling for representation learning on graphs"
    - "verse versatile graph embeddings from similarity measures"
    - "struc2vec learning node representations from structural identity"

Connected through hyperedge HE-8 (induced by source paper "vgraph a generative model for joint community detection and node representation learning"):
  with members:
    - "communitygan community detection with generative adversarial nets"
    - "verse versatile graph embeddings from similarity measures"
    - "gemsec graph embedding with self clustering"
    - "a unified framework for community detection and network representation learning"
    - "node2vec scalable feature learning for networks"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

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)