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: "verse versatile graph embeddings from similarity measures"
  Abstract: Embedding a web-scale information network into a low-dimensional vector space facilitates tasks such as link prediction, classification, and visualization. Past research has addressed the problem of extracting such embeddings by adopting methods from words to graphs, without defining a clearly comprehensible graph-related objective. Yet, as we show, the objectives used in past works implicitly utilize similarity measures among graph nodes. In this paper, we carry the similarity orientation of previous works to its logical conclusion; we propose VERtex Similarity Embeddings (VERSE), a simple, versatile, and memory-efficient method that derives graph embeddings explicitly calibrated to preserve the distributions of a chosen vertex-to-vertex similarity measure. While the default, scalable version of VERSE learns such embeddings by sampling the graph to train a single-layer neural network, we also develop a full variant that does not rely on sampling. Our experimental study on standard benchmarks and real-world datasets demonstrates that VERSE, instantiated with diverse similarity measures, outperforms state-of-the-art methods in terms of precision and recall in major data mining tasks and supersedes them in time and space efficiency, while the scalable sampling-based variant achieves equally good results as the non-scalable full variant.

Connected through hyperedge HE-1 (induced by source paper "graph ordering towards the optimal by learning"):
  with members:
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "deep sets"
    - "representation learning over dynamic graphs"
    - "attention is all you need"
    - "representation learning on graphs methods and applications"
    - "a survey on network embedding"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "network representation learning for link prediction are we improving upon simple heuristics"):
  with members:
    - "network representation learning consolidation and renewed bearing"
    - "evalne a framework for evaluating network embeddings on link prediction"
    - "graph embedding techniques applications and performance a survey"
    - "struc2vec learning node representations from structural identity"
    - "watch your step learning node embeddings via graph attention"
    - "representation learning on graphs methods and applications"
    - "analysis of the impact of negative sampling on link prediction in knowledge graphs"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-3 (induced by source paper "inductive document network embedding with topic word attention"):
  with members:
    - "link prediction with mutual attention for text attributed networks"
    - "global vectors for node representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "node2vec scalable feature learning for networks"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-4 (induced by source paper "deep graph similarity learning a survey"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "deep graph matching consensus"
    - "dynamic graph convolutional networks"
    - "glmnet graph learning matching networks for feature matching"
    - "from community to role based graph embeddings"
    - "learning combinatorial embedding networks for deep graph matching"
    - "community preserving graph convolutions for structural and functional joint embedding of brain networks"
    - "generalizing from a few examples a survey on few shot learning"

Connected through hyperedge HE-5 (induced by source paper "diffusion improves graph learning"):
  with members:
    - "pan path integral based convolution for deep graph neural networks"
    - "graph neural networks with convolutional arma filters"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "simplifying graph convolutional networks"
    - "fast graph representation learning with pytorch geometric"
    - "optimizing generalized pagerank methods for seed expansion community detection"
    - "node embedding with adaptive similarities for scalable learning over graphs"
    - "graph convolutional neural networks for web scale recommender systems"

Connected through hyperedge HE-6 (induced by source paper "homogeneous network embedding for massive graphs via reweighted personalized pagerank"):
  with members:
    - "adversarial training methods for network embedding"
    - "graphvite a high performance cpu gpu hybrid system for node embedding"
    - "netsmf large scale network embedding as sparse matrix factorization"
    - "efficient algorithms for approximate single source personalized pagerank queries"
    - "pytorch biggraph a large scale graph embedding system"
    - "scalable graph embeddings via sparse transpose proximities"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "learning structural node embeddings via diffusion wavelets"

Connected through hyperedge HE-7 (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"
    - "relational inductive biases deep learning and graph networks"
    - "on the dimensionality of word embedding"

Connected through hyperedge HE-8 (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"
    - "fusion graph convolutional networks"
    - "systematic biases in link prediction comparing heuristic and graph embedding based methods"
    - "graph embedding techniques applications and performance a survey"
    - "network representation learning a survey"

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)