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: "graph neural networks with high order feature interactions"
  Abstract: Network representation learning, a fundamental research problem which aims at learning low-dimension node representations on graph-structured data, has been extensively studied in the research community. By generalizing the power of neural networks on graph-structured data, graph neural networks (GNNs) achieve superior capability in network representation learning. However, the node features of many real-world graphs could be high-dimensional and sparse, rendering the learned node representations from existing GNN architectures less expressive. The main reason lies in that those models directly makes use of the raw features of nodes as input for the message-passing and have limited power in capturing sophisticated interactions between features. In this paper, we propose a novel GNN framework for learning node representations that incorporate high-order feature interactions on feature-sparse graphs. Specifically, the proposed message aggregator and feature factorizer extract two channels of embeddings from the feature-sparse graph, characterizing the aggregated node features and high-order feature interactions, respectively. Furthermore, we develop an attentive fusion network to seamlessly combine the information from two different channels and learn the feature interaction-aware node representations. Extensive experiments on various datasets demonstrate the effectiveness of the proposed framework on a variety of graph learning tasks.

Connected through hyperedge HE-1 (induced by source paper "transferring robustness for graph neural network against poisoning attacks"):
  with members:
    - "adversarial examples on graph data deep insights into attack and defense"
    - "power up robust graph convolutional network against evasion attacks based on graph powering"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "graph neural networks with generated parameters for relation extraction"
    - "graph few shot learning via knowledge transfer"
    - "graph convolutional networks with eigenpooling"
    - "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"
    - "learning from multiple cities a meta learning approach for spatial temporal prediction"

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)