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: "multi resolution graph neural network for identifying disease specific variations in brain connectivity"
  Abstract: Convolution Neural Network (CNN) recently have been adopted in several neuroimaging studies for diagnosis capturing disease-specific changes in the brain. While many of these methods are designed to work with images in $\mathbb R^n$ exploiting regular structure of the domain, they are not well-suited to analyze data with irregular structure such as brain connectivity. As there is significant interest in understanding the altered interactions between different brain regions that lead to neuro-disorders, it is important to develop data-driven methods that work with a population of graph data for traditional prediction tasks. In this regime, we propose a novel CNN-based framework with adaptive graph transforms to learn the most disease-relevant connectome feature maps which have the highest discrimination power across diagnostic categories. The backbone of our framework is a multi-resolution representation of the graph matrix which is steered by a set of wavelet-like graph transforms. In this context, our supervised graph learning framework outperforms conventional graph methods that predict diagnostic label only based on the underlying individual graph. Our extensive experiments on two real datasets of functional and structural brain networks show that our multi-resolution framework achieves significantly higher accuracy, precision and recall in predicting diagnostic labels and identifying disease-specific brain connectivities that are associated with brain disorders such as Attention-Deficit/Hyperactivity Disorder (ADHD) and Alzheimer's Disease (AD).

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)