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: "sybilblind detecting fake users in online social networks without manual labels"
  Abstract: Detecting fake users (also called Sybils) in online social networks is a basic security research problem. State-of-the-art approaches rely on a large amount of manually labeled users as a training set. These approaches suffer from three key limitations: 1) it is time-consuming and costly to manually label a large training set, 2) they cannot detect new Sybils in a timely fashion, and 3) they are vulnerable to Sybil attacks that leverage information of the training set. In this work, we propose SybilBlind, a structure-based Sybil detection framework that does not rely on a manually labeled training set. SybilBlind works under the same threat model as state-of-the-art structure-based methods. We demonstrate the effectiveness of SybilBlind using 1) a social network with synthetic Sybils and 2) two Twitter datasets with real Sybils. For instance, SybilBlind achieves an AUC of 0.98 on a Twitter dataset.

Connected through hyperedge HE-1 (induced by source paper "defending against machine learning based inference attacks via adversarial examples opportunities and challenges"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "misleading authorship attribution of source code using adversarial learning"
    - "mockingbird defending against deep learning based website fingerprinting attacks with adversarial traces"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "generative adversarial privacy"
    - "poisoning attacks to graph based recommender systems"
    - "graph based security and privacy analytics via collective classification with joint weight learning and propagation"
    - "adversarial binaries for authorship identification"

Connected through hyperedge HE-2 (induced by source paper "attacking graph based classification via manipulating the graph structure"):
  with members:
    - "adversarial attacks on graph neural networks via meta learning"
    - "certifiable robustness and robust training for graph convolutional networks"
    - "adversarial attack on graph structured data"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "poisoning attacks to graph based recommender systems"
    - "graph based security and privacy analytics via collective classification with joint weight learning and propagation"
    - "structure based sybil detection in social networks via local rule based propagation"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"

Connected through hyperedge HE-3 (induced by source paper "the art of social bots a review and a refined taxonomy"):
  with members:
    - "cashtag piggybacking uncovering spam and bot activity in stock microblogs on twitter"
    - "sybilfuse combining local attributes with global structure to perform robust sybil detection"
    - "lobo evaluation of generalization deficiencies in twitter bot classifiers"
    - "false information on web and social media a survey"
    - "measuring social spam and the effect of bots on information diffusion in social media"
    - "detecting influence campaigns in social networks using the ising model"
    - "botnet campaign detection on twitter"
    - "an unsupervised approach to detect spam campaigns that use botnets on twitter"

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.CR(Cryptography and Security)