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: "regal representation learning based graph alignment"
  Abstract: Problems involving multiple networks are prevalent in many scientific and other domains. In particular, network alignment, or the task of identifying corresponding nodes in different networks, has applications across the social and natural sciences. Motivated by recent advancements in node representation learning for single-graph tasks, we propose REGAL (REpresentation learning-based Graph ALignment), a framework that leverages the power of automatically-learned node representations to match nodes across different graphs. Within REGAL we devise xNetMF, an elegant and principled node embedding formulation that uniquely generalizes to multi-network problems. Our results demonstrate the utility and promise of unsupervised representation learning-based network alignment in terms of both speed and accuracy. REGAL runs up to 30x faster in the representation learning stage than comparable methods, outperforms existing network alignment methods by 20 to 30% accuracy on average, and scales to networks with millions of nodes each.

Connected through hyperedge HE-1 (induced by source paper "deep graph matching consensus"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "learning combinatorial embedding networks for deep graph matching"
    - "cross lingual knowledge graph alignment via graph matching neural network"
    - "computing optimal assignments in linear time for approximate graph matching"
    - "scalable gromov wasserstein learning for graph partitioning and matching"
    - "deep adversarial network alignment"
    - "graph neural networks for user identity linkage"
    - "kergm kernelized graph matching"

Connected through hyperedge HE-2 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs"):
  with members:
    - "a survey on knowledge graphs representation acquisition and applications"
    - "knowledge graph embedding for link prediction a comparative analysis"
    - "multi view knowledge graph embedding for entity alignment"
    - "cross lingual knowledge graph alignment via graph matching neural network"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "jointly learning entity and relation representations for entity alignment"
    - "analyzing the limitations of cross lingual word embedding mappings"
    - "learning to exploit long term relational dependencies in knowledge graphs"

Connected through hyperedge HE-3 (induced by source paper "graph representation learning via graphical mutual information maximization"):
  with members:
    - "gmnn graph markov neural networks"
    - "a two step graph convolutional decoder for molecule generation"
    - "graph wavelet neural network"
    - "relation aware entity alignment for heterogeneous knowledge graphs"
    - "demo net degree specific graph neural networks for node and graph classification"
    - "link prediction based on graph neural networks"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "deep sets"

Connected through hyperedge HE-4 (induced by source paper "adversarial deep network embedding for cross network node classification"):
  with members:
    - "deep network embedding for graph representation learning in signed networks"
    - "adversarial training methods for network embedding"
    - "network together node classification via cross network deep network embedding"
    - "network transfer learning via adversarial domain adaptation with graph convolution"
    - "network embedding for cross network node classification"
    - "label efficient semi supervised learning via graph filtering"
    - "adversarial discriminative domain adaptation"
    - "adversarial network embedding"

Connected through hyperedge HE-5 (induced by source paper "just slaq when you approximate accurate spectral distances for web scale graphs"):
  with members:
    - "network density of states"
    - "ddgk learning graph representations for deep divergence graph kernels"
    - "netlsd hearing the shape of a graph"
    - "a simple yet effective baseline for non attributed graph classification"
    - "approximating the spectrum of a graph"
    - "deltacon a principled massive graph similarity function"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-6 (induced by source paper "elruna elimination rule based network alignment"):
  with members:
    - "an iterative global structure assisted labeled network aligner"
    - "spectral alignment of graphs"
    - "advanced coarsening schemes for graph partitioning"
    - "relaxation based coarsening and multiscale graph organization"

Connected through hyperedge HE-7 (induced by source paper "from community to role based graph embeddings"):
  with members:
    - "node2bits compact time and attribute aware node representations for user stitching"
    - "similarity learning with higher order graph convolutions for brain network analysis"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "overlapping community detection with graph neural networks"
    - "feature propagation on graph a new perspective to graph representation learning"
    - "graph embedding techniques applications and performance a survey"
    - "higher order graph convolutional networks"
    - "network representation learning a survey"

Connected through hyperedge HE-8 (induced by source paper "network transfer learning via adversarial domain adaptation with graph convolution"):
  with members:
    - "deep network embedding for graph representation learning in signed networks"
    - "adversarial training methods for network embedding"
    - "beyond sharing weights for deep domain adaptation"
    - "attributed graph clustering via adaptive graph convolution"
    - "network embedding for cross network node classification"
    - "graph neural networks for social recommendation"
    - "label efficient semi supervised learning via graph filtering"
    - "deep visual domain adaptation 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)