human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "structure based sybil detection in social networks via local rule based propagation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "attacking graph based classification via manipulating the graph structure")
  Member "sybilfuse combining local attributes with global structure to perform robust sybil detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "defending against machine learning based inference attacks via adversarial examples opportunities and challenges")
  Member "sybilbelief a semi supervised learning approach for structure based sybil detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "defending against machine learning based inference attacks via adversarial examples opportunities and challenges")
  Member "social turing tests crowdsourcing sybil detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "uncovering coordinated networks on social media")
  Member "uncovering social network sybils in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fawkes protecting personal privacy against unauthorized deep learning models")

Question: Please tell me which class the source paper 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)