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: "distance metric learning using graph convolutional networks application to functional brain networks"
  Abstract: Evaluating similarity between graphs is of major importance in several computer vision and pattern recognition problems, where graph representations are often used to model objects or interactions between elements. The choice of a distance or similarity metric is, however, not trivial and can be highly dependent on the application at hand. In this work, we propose a novel metric learning method to evaluate distance between graphs that leverages the power of convolutional neural networks, while exploiting concepts from spectral graph theory to allow these operations on irregular graphs. We demonstrate the potential of our method in the field of connectomics, where neuronal pathways or functional connections between brain regions are commonly modelled as graphs. In this problem, the definition of an appropriate graph similarity function is critical to unveil patterns of disruptions associated with certain brain disorders. Experimental results on the ABIDE dataset show that our method can learn a graph similarity metric tailored for a clinical application, improving the performance of a simple k-nn classifier by 11.9% compared to a traditional distance metric.

Connected through hyperedge HE-1 (induced by source paper "understanding graph isomorphism network for brain mr functional connectivity analysis"):
  with members:
    - "understanding geometry of encoder decoder cnns"
    - "graph neural network for interpreting task fmri biomarkers"
    - "a comprehensive survey on graph neural networks"
    - "similarity learning with higher order graph convolutions for brain network analysis"
    - "representation learning on graphs with jumping knowledge networks"
    - "how powerful are graph neural networks"
    - "graph saliency maps through spectral convolutional networks application to sex classification with brain connectivity"
    - "similarity learning with higher order proximity for brain network analysis"

Connected through hyperedge HE-2 (induced by source paper "generative models for automatic chemical design"):
  with members:
    - "selfies a robust representation of semantically constrained graphs with an example application in chemistry"
    - "a model to search for synthesizable molecules"
    - "all smiles variational autoencoder"
    - "likelihood free inference and generation of molecular graphs"
    - "molecular geometry prediction using a deep generative graph neural network"
    - "labeled graph generative adversarial networks"
    - "decoding molecular graph embeddings with reinforcement learning"
    - "scaffold based molecular design using graph generative model"

Connected through hyperedge HE-3 (induced by source paper "learning context graph for person search"):
  with members:
    - "person re identification with deep similarity guided graph neural network"
    - "person search via a mask guided two stream cnn model"
    - "learning human object interactions by graph parsing neural networks"
    - "video salient object detection via fully convolutional networks"
    - "videos as space time region graphs"
    - "unifying identification and context learning for person recognition"
    - "person re identification via recurrent feature aggregation"
    - "a discriminatively learned cnn embedding for person reidentification"

Connected through hyperedge HE-4 (induced by source paper "graph transformer networks"):
  with members:
    - "kgat knowledge graph attention network for recommendation"
    - "self attention graph pooling"
    - "graph wavelet neural network"
    - "link prediction based on graph neural networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "geniepath graph neural networks with adaptive receptive paths"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"

Connected through hyperedge HE-5 (induced by source paper "personalized hashtag recommendation for micro videos"):
  with members:
    - "kgat knowledge graph attention network for recommendation"
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "neural graph collaborative filtering"
    - "graph convolutional neural networks for web scale recommender systems"
    - "adversarial personalized ranking for recommendation"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "learning deep generative models of graphs"

Connected through hyperedge HE-6 (induced by source paper "brain signal classification via learning connectivity structure"):
  with members:
    - "classification of eeg based brain connectivity networks in schizophrenia using a multi domain connectome convolutional neural network"
    - "a dilated inception network for visual saliency prediction"
    - "learning discrete structures for graph neural networks"
    - "a comprehensive survey on graph neural networks"
    - "convolutional neural network approach for eeg based emotion recognition using brain connectivity and its spatial information"
    - "relational inductive biases deep learning and graph networks"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-7 (induced by source paper "unsupervised inductive graph level representation learning via graph graph proximity"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "a comprehensive survey on graph neural networks"
    - "link prediction based on graph neural networks"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "drug similarity integration through attentive multi view graph auto encoders"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"

Connected through hyperedge HE-8 (induced by source paper "graph neural network for interpreting task fmri biomarkers"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "sanity checks for saliency maps"
    - "neural message passing for quantum chemistry"
    - "semi supervised classification with graph convolutional networks"

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.CV(Computer Vision and Pattern Recognition)