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: "semi supervised user geolocation via graph convolutional networks"
  Abstract: Social media user geolocation is vital to many applications such as event detection. In this paper, we propose GCN, a multiview geolocation model based on Graph Convolutional Networks, that uses both text and network context. We compare GCN to the state-of-the-art, and to two baselines we propose, and show that our model achieves or is competitive with the state- of-the-art over three benchmark geolocation datasets when sufficient supervision is available. We also evaluate GCN under a minimal supervision scenario, and show it outperforms baselines. We find that highway network gates are essential for controlling the amount of useful neighbourhood expansion in GCN.

Connected through hyperedge HE-1 (induced by source paper "deepgcns making gcns go as deep as cnns"):
  with members:
    - "cluster gcn an efficient algorithm for training deep and large graph convolutional networks"
    - "fast graph representation learning with pytorch geometric"
    - "a comprehensive survey on graph neural networks"
    - "deepgcns can gcns go as deep as cnns"
    - "graph neural networks a review of methods and applications"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "graph convolutional neural networks for web scale recommender systems"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"

Connected through hyperedge HE-2 (induced by source paper "hypergraph convolution and hypergraph attention"):
  with members:
    - "link prediction based on graph neural networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "graph neural networks a review of methods and applications"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "adversarial attack on graph structured data"
    - "graph convolutional neural networks for web scale recommender systems"
    - "constrained graph variational autoencoders for molecule design"
    - "hierarchical graph representation learning with differentiable pooling"

Connected through hyperedge HE-3 (induced by source paper "deepgcns can gcns go as deep as cnns"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "graph convolutional neural networks for web scale recommender systems"
    - "graph r cnn for scene graph generation"
    - "representation learning on graphs with jumping knowledge networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "dynamic graph cnn for learning on point clouds"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-4 (induced by source paper "explainability techniques for graph convolutional networks"):
  with members:
    - "graph networks as learnable physics engines for inference and control"
    - "interpreting neural network judgments via minimal stable and symbolic corrections"
    - "graph to sequence learning using gated graph neural networks"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "relational inductive biases deep learning and graph networks"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "explaining explanations an overview of interpretability of machine learning"
    - "learning human object interactions by graph parsing neural networks"

Connected through hyperedge HE-5 (induced by source paper "simplifying graph convolutional networks"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "label efficient semi supervised learning via graph filtering"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "power up robust graph convolutional network against evasion attacks based on graph powering"):
  with members:
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "adversarial attacks on graph neural networks via meta learning"
    - "a comprehensive survey on graph neural networks"
    - "can adversarial network attack be defended"
    - "graph powering and spectral robustness"
    - "graph neural networks a review of methods and applications"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "adversarial attack on graph structured data"

Connected through hyperedge HE-7 (induced by source paper "an anatomy of graph neural networks going deep via the lens of mutual information exponential decay vs full preservation"):
  with members:
    - "graph neural networks exponentially lose expressive power for node classification"
    - "on asymptotic behaviors of graph cnns from dynamical systems perspective"
    - "simplifying graph convolutional networks"
    - "a comprehensive survey on graph neural networks"
    - "deepgcns can gcns go as deep as cnns"
    - "graph neural networks a review of methods and applications"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graph to sequence learning using gated graph neural networks"

Connected through hyperedge HE-8 (induced by source paper "jointly learning entity and relation representations for entity alignment"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "relation aware entity alignment for heterogeneous knowledge graphs"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "co training embeddings of knowledge graphs and entity descriptions for cross lingual entity alignment"
    - "adversarial training for multi context joint entity and relation extraction"
    - "variational reasoning for question answering with knowledge graph"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "multilingual knowledge graph embeddings for cross lingual knowledge alignment"

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.CL(Computation and Language)