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: "semi supervised graph classification a hierarchical graph perspective"
  Abstract: Node classification and graph classification are two graph learning problems that predict the class label of a node and the class label of a graph respectively. A node of a graph usually represents a real-world entity, e.g., a user in a social network, or a protein in a protein-protein interaction network. In this work, we consider a more challenging but practically useful setting, in which a node itself is a graph instance. This leads to a hierarchical graph perspective which arises in many domains such as social network, biological network and document collection. For example, in a social network, a group of people with shared interests forms a user group, whereas a number of user groups are interconnected via interactions or common members. We study the node classification problem in the hierarchical graph where a `node' is a graph instance, e.g., a user group in the above example. As labels are usually limited in real-world data, we design two novel semi-supervised solutions named \underline{SE}mi-supervised gr\underline{A}ph c\underline{L}assification via \underline{C}autious/\underline{A}ctive \underline{I}teration (or SEAL-C/AI in short). SEAL-C/AI adopt an iterative framework that takes turns to build or update two classifiers, one working at the graph instance level and the other at the hierarchical graph level. To simplify the representation of the hierarchical graph, we propose a novel supervised, self-attentive graph embedding method called SAGE, which embeds graph instances of arbitrary size into fixed-length vectors. Through experiments on synthetic data and Tencent QQ group data, we demonstrate that SEAL-C/AI not only outperform competing methods by a significant margin in terms of accuracy/Macro-F1, but also generate meaningful interpretations of the learned representations.

Connected through hyperedge HE-1 (induced by source paper "robustness verification for transformers"):
  with members:
    - "correctness verification of neural networks"
    - "adversarial examples with difficult common words for paraphrase identification"
    - "certified robustness to adversarial word substitutions"
    - "verifying aircraft collision avoidance neural networks through linear approximations of safe regions"
    - "art abstraction refinement guided training for provably correct neural networks"
    - "achieving verified robustness to symbol substitutions via interval bound propagation"
    - "verification of non linear specifications for neural networks"
    - "visualbert a simple and performant baseline for vision and language"

Connected through hyperedge HE-2 (induced by source paper "spectral graph attention network"):
  with members:
    - "power up robust graph convolutional network against evasion attacks based on graph powering"
    - "stabilizing gans with octave convolutions"
    - "a restricted black box adversarial framework towards attacking graph embedding models"
    - "lanczosnet multi scale deep graph convolutional networks"
    - "measuring and relieving the over smoothing problem for graph neural networks from the topological view"
    - "graph neural networks with convolutional arma filters"
    - "dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems"
    - "graph wavelet neural network"

Connected through hyperedge HE-3 (induced by source paper "adversarial attack on community detection by hiding individuals"):
  with members:
    - "gap generalizable approximate graph partitioning framework"
    - "a comprehensive survey on graph neural networks"
    - "adversarial attack on graph structured data"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "constrained graph variational autoencoders for molecule design"
    - "ga based q attack on community detection"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "adversarial attack and defense on graph data a survey"

Connected through hyperedge HE-4 (induced by source paper "graph preserving grid layout a simple graph drawing method for graph classification using cnns"):
  with members:
    - "a deep generative model for graph layout"
    - "graph u nets"
    - "simplifying graph convolutional networks"
    - "learning discrete structures for graph neural networks"
    - "graph star net for generalized multi task learning"
    - "a comprehensive survey on graph neural networks"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "graphrnn generating realistic graphs with deep auto regressive models"

Connected through hyperedge HE-5 (induced by source paper "predicting path failure in time evolving graphs"):
  with members:
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "a structured self attentive sentence embedding"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"
    - "semi supervised classification with graph convolutional networks"
    - "deepcas an end to end predictor of information cascades"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "deepwalk online learning of social representations"

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)