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: "hiding individuals and communities in a social network"
  Abstract: The Internet and social media have fuelled enormous interest in social network analysis. New tools continue to be developed and used to analyse our personal connections, with particular emphasis on detecting communities or identifying key individuals in a social network. This raises privacy concerns that are likely to exacerbate in the future. With this in mind, we ask the question ‘Can individuals or groups actively manage their connections to evade social network analysis tools?’ By addressing this question, the general public may better protect their privacy, oppressed activist groups may better conceal their existence and security agencies may better understand how terrorists escape detection. We first study how an individual can evade ‘node centrality’ analysis while minimizing the negative impact that this may have on his or her influence. We prove that an optimal solution to this problem is difficult to compute. Despite this hardness, we demonstrate how even a simple heuristic, whereby attention is restricted to the individual’s immediate neighbourhood, can be surprisingly effective in practice; for example, it could easily disguise Mohamed Atta’s leading position within the World Trade Center terrorist network. We also study how a community can increase the likelihood of being overlooked by community-detection algorithms. We propose a measure of concealment—expressing how well a community is hidden—and use it to demonstrate the effectiveness of a simple heuristic, whereby members of the community either ‘unfriend’ certain other members or ‘befriend’ some non-members in a coordinated effort to camouflage their community. Waniek and colleagues show that individuals and communities can disguise themselves from detection online by standard social network analysis tools through simple changes to their social network connections.

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"
    - "ga based q attack on community detection"
    - "adversarial examples that fool both computer vision and time limited humans"

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"
    - "ga based q attack on community detection"
    - "normalized cut loss for weakly supervised cnn segmentation"

Connected through hyperedge HE-5 (induced by source paper "towards an efficient and general framework of robust training for graph neural networks"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "on the design of black box adversarial examples by leveraging gradient free optimization and operator splitting method"
    - "zo adamm zeroth order adaptive momentum method for black box optimization"
    - "adversarial attacks on graph neural networks via meta learning"
    - "certifiable robustness and robust training for graph convolutional networks"
    - "interpreting adversarial examples by activation promotion and suppression"
    - "adversarial attack on graph structured data"
    - "zeroth order stochastic variance reduction for nonconvex optimization"

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"
    - "ga based q attack on community detection"
    - "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"
    - "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 "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"
    - "target defense against link prediction based attacks via evolutionary perturbations"
    - "practical black box attacks against machine learning"
    - "node2vec scalable feature learning for networks"
    - "universal adversarial perturbations"

Connected through hyperedge HE-8 (induced by source paper "your stance is exposed analysing possible factors for stance detection on social media"):
  with members:
    - "unsupervised user stance detection on twitter"
    - "assessing sentiment of the expressed stance on social media"
    - "automatic stance detection using end to end memory networks"
    - "topical stance detection for twitter a two phase lstm model using attention"
    - "discourse aware rumour stance classification in social media using sequential classifiers"
    - "friends and enemies of clinton and trump using context for detecting stance in political tweets"
    - "reducing controversy by connecting opposing views"
    - "stance detection with bidirectional conditional encoding"

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)