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: "ga based q attack on community detection"
  Abstract: Community detection plays an important role in social networks, since it can help to naturally divide the network into smaller parts so as to simplify network analysis. However, on the other hand, it arises the concern that individual information may be over-mined, and the concept community deception thus is proposed to protect individual privacy on social networks. Here, we introduce and formalize the problem of community detection attack and develop efficient strategies to attack community detection algorithms by rewiring a small number of connections, leading to individual privacy protection. In particular, we first give two heuristic attack strategies, i.e., Community Detection Attack (CDA) and Degree Based Attack (DBA), as baselines, utilizing the information of detected community structure and node degree, respectively. And then we propose a Genetic Algorithm (GA) based Q-Attack, where the modularity Q is used to design the fitness function. We launch community detection attack based on the above three strategies against three modularity based community detection algorithms on two social networks. By comparison, our Q-Attack method achieves much better attack effects than CDA and DBA, in terms of the larger reduction of both modularity Q and Normalized Mutual Information (NMI). Besides, we find that the adversarial networks obtained by Q-Attack on a specific community detection algorithm can be still effective on others, no matter whether they are modularity based or not, indicating its strong transferability.

Connected through hyperedge HE-1 (induced by source paper "adversarial attacks and defenses on graphs a review and empirical study"):
  with members:
    - "certified robustness of community detection against adversarial structural perturbation via randomized smoothing"
    - "adversarial attack on community detection by hiding individuals"
    - "transferring robustness for graph neural network against poisoning attacks"
    - "graph universal adversarial attacks a few bad actors ruin graph learning models"
    - "adversarial training methods for network embedding"
    - "adversarial examples on graph data deep insights into attack and defense"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "the general black box attack method for graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing"):
  with members:
    - "adversarial examples on graph data deep insights into attack and defense"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "certified adversarial robustness via randomized smoothing"
    - "certified robustness for top k predictions against adversarial perturbations via randomized smoothing"
    - "adversarial attacks on graph neural networks via meta learning"
    - "certifiable robustness to graph perturbations"
    - "attacking graph based classification via manipulating the graph structure"

Connected through hyperedge HE-3 (induced by source paper "mga momentum gradient attack on network"):
  with members:
    - "unsupervised euclidean distance attack on network embedding"
    - "things you may not know about adversarial example a black box adversarial image attack"
    - "adversarial attack on graph structured data"
    - "adversarial training towards robust multimedia recommender system"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "poisoning attacks to graph based recommender systems"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "curriculum adversarial training"

Connected through hyperedge HE-4 (induced by source paper "adversarial attack on community detection by hiding individuals"):
  with members:
    - "gap generalizable approximate graph partitioning framework"
    - "semi supervised graph classification a hierarchical graph perspective"
    - "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"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "adversarial attack and defense on graph data a survey"

Connected through hyperedge HE-5 (induced by source paper "modularity deltas a semi global centrality measure for complex networks"):
  with members:
    - "from louvain to leiden guaranteeing well connected communities"

Connected through hyperedge HE-6 (induced by source paper "unsupervised euclidean distance attack on network embedding"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "attack graph convolutional networks by adding fake nodes"
    - "data poisoning attack against unsupervised node embedding methods"
    - "target defense against link prediction based attacks via evolutionary perturbations"
    - "hiding individuals and communities in a social network"
    - "synthesizing robust adversarial examples"
    - "node2vec scalable feature learning for networks"
    - "don t walk skip online learning of multi scale network embeddings"

Connected through hyperedge HE-7 (induced by source paper "adversarial enhancement for community detection in complex networks"):
  with members:
    - "edmot an edge enhancement approach for motif aware community detection"
    - "adversarial attack on graph structured data"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "target defense against link prediction based attacks via evolutionary perturbations"
    - "hiding individuals and communities in a social network"
    - "node2vec scalable feature learning for networks"
    - "enhancing community detection using a network weighting strategy"
    - "efficient and principled method for detecting communities in networks"

Connected through hyperedge HE-8 (induced by source paper "multiscale evolutionary perturbation attack on community detection"):
  with members:
    - "adversarial attacks on node embeddings via graph poisoning"
    - "hiding individuals and communities in a social network"
    - "interplay between social influence and network centrality a comparative study on shapley centrality and single node influence centrality"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "clustering and community detection in directed networks a survey"
    - "multistep greedy algorithm identifies community structure in real world and computer generated 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)