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: "target defense against link prediction based attacks via evolutionary perturbations"
  Abstract: In social networks, by removing some target-sensitive links, privacy protection might be achieved. However, some hidden links can still be re-observed by link prediction methods on observable networks. In this paper, the conventional link prediction method named Resource Allocation Index (RA) is adopted for privacy attacks. Several defense methods are proposed, including heuristic and evolutionary approaches, to protect targeted links from RA attacks via evolutionary perturbations. This is the first time to study privacy protection on targeted links against link-prediction-based attacks. Some links are randomly selected from the network as targeted links for experimentation. The simulation results on six real-world networks demonstrate the superiority of the evolutionary perturbation approach for target defense against RA attacks. Moreover, transferring experiments show that, although the evolutionary perturbation approach is designed to against RA attacks, it is also effective against other link-prediction-based attacks.

Connected through hyperedge HE-1 (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"
    - "ga based q attack on community detection"
    - "adversarial examples that fool both computer vision and time limited humans"

Connected through hyperedge HE-2 (induced by source paper "unsupervised euclidean distance attack on network embedding"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "ga based q attack on community detection"
    - "attack graph convolutional networks by adding fake nodes"
    - "data poisoning attack against unsupervised node embedding methods"
    - "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-3 (induced by source paper "can adversarial network attack be defended"):
  with members:
    - "adversarial attacks and defences competition"
    - "adversarial attack on graph structured data"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "hiding individuals and communities in a social network"
    - "practical black box attacks against machine learning"
    - "node2vec scalable feature learning for networks"
    - "universal adversarial perturbations"

Connected through hyperedge HE-4 (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"
    - "ga based q attack on community detection"
    - "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-5 (induced by source paper "manipulating node similarity measures in network"):
  with members:
    - "covert networks how hard is it to hide"
    - "k core minimization a game theoretic approach"
    - "influence minimization under budget and matroid constraints extended version"
    - "attack tolerance of link prediction algorithms how to hide your relations in a social network"
    - "link prediction in social networks the state of the art"

Connected through hyperedge HE-6 (induced by source paper "ga based q attack on community detection"):
  with members:
    - "adversarial attack on graph structured data"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "hiding individuals and communities in a social network"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "attacking similarity based link prediction in social networks"):
  with members:
    - "link prediction based on graph neural networks"
    - "attack tolerance of link prediction algorithms how to hide your relations in a social network"
    - "hiding individuals and communities in a social network"
    - "link prediction in social networks the state of the art"

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)