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: "graph embedding techniques applications and performance a survey"
  Abstract: Abstract   Graphs, such as social networks, word co-occurrence networks, and communication networks, occur naturally in various real-world applications. Analyzing them yields insight into the structure of society, language, and different patterns of communication. Many approaches have been proposed to perform the analysis. Recently, methods which use the representation of graph nodes in vector space have gained traction from the research community. In this survey, we provide a comprehensive and structured analysis of various graph embedding techniques proposed in the literature. We first introduce the embedding task and its challenges such as scalability, choice of dimensionality, and features to be preserved, and their possible solutions. We then present three categories of approaches based on factorization methods, random walks, and deep learning, with examples of representative algorithms in each category and analysis of their performance on various tasks. We evaluate these state-of-the-art methods on a few common datasets and compare their performance against one another. Our analysis concludes by suggesting some potential applications and future directions. We finally present the open-source Python library we developed, named GEM (Graph Embedding Methods, available at  https://github.com/palash1992/GEM ), which provides all presented algorithms within a unified interface to foster and facilitate research on the topic.

Connected through hyperedge HE-1 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks"):
  with members:
    - "arbitrary shape scene text detection with adaptive text region representation"
    - "look more than once an accurate detector for text of arbitrary shapes"
    - "textfield learning a deep direction field for irregular scene text detection"
    - "omnidirectional scene text detection with sequential free box discretization"
    - "character region awareness for text detection"
    - "deep visual template free form parsing"
    - "msr multi scale shape regression for scene text detection"
    - "rethinking table recognition using graph neural networks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "a survey of adversarial learning on graphs"):
  with members:
    - "heterogeneous graph neural networks for malicious account detection"
    - "dynamic graph convolutional networks"
    - "multiscale evolutionary perturbation attack on community detection"
    - "power up robust graph convolutional network against evasion attacks based on graph powering"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "time aware gradient attack on dynamic network link prediction"
    - "a restricted black box adversarial framework towards attacking graph embedding models"
    - "adversarial robustness of similarity based link prediction"

Connected through hyperedge HE-4 (induced by source paper "permutohedral gcn graph convolutional networks with global attention"):
  with members:
    - "reformer the efficient transformer"
    - "geom gcn geometric graph convolutional networks"
    - "measuring and relieving the over smoothing problem for graph neural networks from the topological view"
    - "permutohedral attention module for efficient non local neural networks"
    - "learning task specific generalized convolutions in the permutohedral lattice"
    - "graph u nets"
    - "position aware graph neural networks"
    - "hierarchical graph representation learning with differentiable pooling"

Connected through hyperedge HE-5 (induced by source paper "everything about you a multimodal approach towards friendship inference in online social networks"):
  with members:
    - "language in our time an empirical analysis of hashtags"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "you are your metadata identification and obfuscation of social media users using metadata information"
    - "a hybrid model for identity obfuscation by face replacement"
    - "attriguard a practical defense against attribute inference attacks via adversarial machine learning"
    - "friendships rivalries and trysts characterizing relations between ideas in texts"
    - "connecting pixels to privacy and utility automatic redaction of private information in images"
    - "walk2friends inferring social links from mobility profiles"

Connected through hyperedge HE-6 (induced by source paper "supervised domain adaptation using graph embedding"):
  with members:
    - "d sne domain adaptation using stochastic neighborhood embedding"
    - "hark side of deep learning from grad student descent to automated machine learning"
    - "deep visual domain adaptation a survey"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "fine tuned language models for text classification"
    - "universal language model fine tuning for text classification"
    - "unified deep supervised domain adaptation and generalization"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-7 (induced by source paper "exem expert embedding using dominating set theory with deep learning approaches"):
  with members:
    - "dyngraph2vec capturing network dynamics using dynamic graph representation learning"
    - "relational representation learning for dynamic knowledge graphs a survey"
    - "community aware random walk for network embedding"
    - "dynnode2vec scalable dynamic network embedding"
    - "dynamic graph representation learning via self attention networks"
    - "attributed social network embedding"
    - "network representation learning a survey"
    - "a survey on network embedding"

Connected through hyperedge HE-8 (induced by source paper "learning distributed representations of graphs with geo2dr"):
  with members:
    - "on graph classification networks datasets and baselines"
    - "deep graph library towards efficient and scalable deep learning on graphs"
    - "fast graph representation learning with pytorch geometric"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"
    - "anonymous walk embeddings"
    - "neural message passing for quantum chemistry"
    - "graph2vec learning distributed representations of graphs"

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)