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: "geniepath graph neural networks with adaptive receptive paths"
  Abstract: We present, GeniePath, a scalable approach for learning adaptive receptive fields of neural networks defined on permutation invariant graph data. In GeniePath, we propose an adaptive path layer consists of two functions designed for breadth and depth exploration respectively, where the former learns the importance of different sized neighborhoods, while the latter extracts and filters signals aggregated from neighbors of different hops away. Our method works both in transductive and inductive settings, and extensive experiments compared with state-of-the-art methods show that our approaches are useful especially on large graph.

Connected through hyperedge HE-1 (induced by source paper "adaptive propagation graph convolutional network"):
  with members:
    - "constant time graph neural networks"
    - "graph neural networks with convolutional arma filters"
    - "diffusion improves graph learning"
    - "fast graph representation learning with pytorch geometric"
    - "a gentle introduction to deep learning for graphs"
    - "a comprehensive survey on graph neural networks"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "weisfeiler and leman go neural higher order graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "heterogeneous graph neural networks for malicious account detection"):
  with members:
    - "representation learning on graphs methods and applications"
    - "xgboost a scalable tree boosting system"
    - "identity mappings in deep residual networks"
    - "node2vec scalable feature learning for networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "discriminative embeddings of latent variable models for structured data"
    - "deepwalk online learning of social representations"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "infdetect a large scale graph based fraud detection system for e commerce insurance"):
  with members:
    - "heterogeneous graph neural networks for malicious account detection"
    - "graph embedding techniques applications and performance a survey"
    - "node2vec scalable feature learning for networks"
    - "discriminative embeddings of latent variable models for structured data"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "survey of insurance fraud detection using data mining techniques"

Connected through hyperedge HE-4 (induced by source paper "graph representation learning for merchant incentive optimization in mobile payment marketing"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "representation learning on graphs methods and applications"
    - "xgboost a scalable tree boosting system"
    - "tensorflow a system for large scale machine learning"
    - "deep learning in neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (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-6 (induced by source paper "sgas sequential greedy architecture search"):
  with members:
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "kpconv flexible and deformable convolution for point clouds"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "bayesnas a bayesian approach for neural architecture search"
    - "evaluating the search phase of neural architecture search"
    - "fast graph representation learning with pytorch geometric"
    - "deepgcns can gcns go as deep as cnns"
    - "deepgcns making gcns go as deep as cnns"

Connected through hyperedge HE-7 (induced by source paper "improving graph attention networks with large margin based constraints"):
  with members:
    - "simplifying graph convolutional networks"
    - "multi hop reading comprehension across multiple documents by reasoning over heterogeneous graphs"
    - "a comprehensive survey on graph neural networks"
    - "link prediction based on graph neural networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "deeply learning molecular structure property relationships using attention and gate augmented graph convolutional network"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"

Connected through hyperedge HE-8 (induced by source paper "graph few shot learning via knowledge transfer"):
  with members:
    - "transferring robustness for graph neural network against poisoning attacks"
    - "transferable end to end aspect based sentiment analysis with selective adversarial learning"
    - "learning from multiple cities a meta learning approach for spatial temporal prediction"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "hierarchically structured meta learning"
    - "hierarchical graph representation learning with differentiable pooling"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "large scale learnable 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.LG(Machine Learning)