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: "label efficient semi supervised learning via graph filtering"
  Abstract: Graph-based methods have been demonstrated as one of the most effective approaches for semi-supervised learning, as they can exploit the connectivity patterns between labeled and unlabeled data samples to improve learning performance. However, existing graph-based methods either are limited in their ability to jointly model graph structures and data features, such as the classical label propagation methods, or require a considerable amount of labeled data for training and validation due to high model complexity, such as the recent neural-network-based methods. In this paper, we address label efficient semi-supervised learning from a graph filtering perspective. Specifically, we propose a graph filtering framework that injects graph similarity into data features by taking them as signals on the graph and applying a low-pass graph filter to extract useful data representations for classification, where label efficiency can be achieved by conveniently adjusting the strength of the graph filter. Interestingly, this framework unifies two seemingly very different methods -- label propagation and graph convolutional networks. Revisiting them under the graph filtering framework leads to new insights that improve their modeling capabilities and reduce model complexity. Experiments on various semi-supervised classification tasks on four citation networks and one knowledge graph and one semi-supervised regression task for zero-shot image recognition validate our findings and proposals.

Connected through hyperedge HE-1 (induced by source paper "bridging the gap between spatial and spectral domains a survey on graph neural networks"):
  with members:
    - "anti money laundering in bitcoin experimenting with graph convolutional networks for financial forensics"
    - "graph neural networks with convolutional arma filters"
    - "a fair comparison of graph neural networks for graph classification"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "what graph neural networks cannot learn depth vs width"
    - "gnn explainer a tool for post hoc explanation of graph neural networks"
    - "simplifying graph convolutional networks"
    - "learning individual treatment effects from networked observational data"

Connected through hyperedge HE-2 (induced by source paper "adaptive graph convolutional network with attention graph clustering for co saliency detection"):
  with members:
    - "scene graph generation with external knowledge and image reconstruction"
    - "learning graph embedding with adversarial training methods"
    - "attributed graph clustering a deep attentional embedding approach"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "review of visual saliency detection with comprehensive information"
    - "iterative visual reasoning beyond convolutions"
    - "adaptive graph convolutional neural networks"

Connected through hyperedge HE-3 (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"
    - "regal representation learning based graph alignment"
    - "adversarial discriminative domain adaptation"
    - "adversarial network embedding"

Connected through hyperedge HE-4 (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"
    - "regal representation learning based graph alignment"
    - "deep visual domain adaptation a survey"

Connected through hyperedge HE-5 (induced by source paper "simplifying graph convolutional networks"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "a comprehensive survey on graph neural networks"
    - "classifier and exemplar synthesis for zero shot learning"
    - "graph neural networks a review of methods and applications"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "attention models in graphs a survey"

Connected through hyperedge HE-6 (induced by source paper "attributed graph learning with 2 d graph convolution"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "gmnn graph markov neural networks"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "attributed graph clustering via adaptive graph convolution"
    - "learning discrete structures for graph neural networks"
    - "a flexible generative framework for graph based semi supervised learning"
    - "graph neural networks a review of methods and applications"
    - "deep learning on graphs a survey"

Connected through hyperedge HE-7 (induced by source paper "graphzoom a multi level spectral approach for accurate and scalable graph embedding"):
  with members:
    - "network embedding on compression and learning"
    - "initialization for network embedding a graph partition approach"
    - "learning topological representation for networks via hierarchical sampling"
    - "graph neural networks a review of methods and applications"
    - "mile a multi level framework for scalable graph embedding"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graph embedding techniques applications and performance a survey"
    - "cosine compressive network embedding on large scale information networks"

Connected through hyperedge HE-8 (induced by source paper "shoestring graph based semi supervised learning with severely limited labeled data"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "prototypical networks for few shot learning"
    - "a simple neural attentive meta learner"
    - "semi supervised classification with graph convolutional networks"
    - "matching networks for one shot learning"
    - "deep residual learning for image recognition"

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.LG(Machine Learning)