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 dimensional graph convolutional networks"
  Abstract: Convolutional neural networks (CNNs) leverage the great power in representation learning on regular grid data such as image and video. Recently, increasing attention has been paid on generalizing CNNs to graph or network data which is highly irregular. Some focus on graph-level representation learning while others aim to learn node-level representations. These methods have been shown to boost the performance of many graph-level tasks such as graph classification and node-level tasks such as node classification. Most of these methods have been designed for single-dimensional graphs where a pair of nodes can only be connected by one type of relation. However, many real-world graphs have multiple types of relations and they can be naturally modeled as multi-dimensional graphs with each type of relation as a dimension. Multi-dimensional graphs bring about richer interactions between dimensions, which poses tremendous challenges to the graph convolutional neural networks designed for single-dimensional graphs. In this paper, we study the problem of graph convolutional networks for multi-dimensional graphs and propose a multi-dimensional convolutional neural network model mGCN aiming to capture rich information in learning node-level representations for multi-dimensional graphs. Comprehensive experiments on real-world multi-dimensional graphs demonstrate the effectiveness of the proposed framework.

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"
    - "graph neural networks with high order feature interactions"
    - "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"

Connected through hyperedge HE-2 (induced by source paper "graph convolutional networks with eigenpooling"):
  with members:
    - "graph u nets"
    - "graph neural networks for social recommendation"
    - "graph representation learning via hard and channel wise attention networks"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"

Connected through hyperedge HE-3 (induced by source paper "learning cross domain representation with multi graph neural network"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "graph convolutional neural networks for web scale recommender systems"
    - "learning and transferring ids representation in e commerce"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "how powerful are graph neural networks"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "representation learning on graphs methods and applications"
    - "a survey on network embedding"

Connected through hyperedge HE-4 (induced by source paper "unsupervised attributed multiplex network embedding"):
  with members:
    - "self attention graph pooling"
    - "lovasz convolutional networks"
    - "mvn2vec preservation and collaboration in multi view network embedding"
    - "mine mutual information neural estimation"
    - "struc2vec learning node representations from structural identity"
    - "an attention based collaboration framework for multi view network representation learning"
    - "principled multilayer network embedding"
    - "attributed network embedding for learning in a dynamic environment"

Connected through hyperedge HE-5 (induced by source paper "graph neural networks for social recommendation"):
  with members:
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "graph convolutional neural networks for web scale recommender systems"
    - "neural collaborative filtering"
    - "item silk road recommending items from information domains to social users"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "a survey on dialogue systems recent advances and new frontiers"
    - "geometric deep learning going beyond euclidean data"
    - "node2vec scalable feature learning for 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.SI(Social and Information Networks)