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: "structure based sybil detection in social networks via local rule based propagation"
  Abstract: Sybil detection in social networks is a basic security research problem. Structure-based methods have been shown to be promising at detecting Sybils. Existing structure-based methods can be classified into Random Walk (RW)-based methods and Loop Belief Propagation (LBP)-based methods. RW-based methods cannot leverage labeled Sybils and labeled benign users simultaneously, which limits their detection accuracy, and/or they are not robust to noisy labels. LBP-based methods are not scalable and cannot guarantee convergence. In this work, we propose SybilSCAR, a novel structure-based method to detect Sybils in social networks. SybilSCAR is Scalable, Convergent, Accurate, and Robust to label noise. We first propose a framework to unify RW-based and LBP-based methods. Under our framework, these methods can be viewed as iteratively applying a (different) local rule to every user, which propagates label information among a social graph. Second, we design a new local rule, which SybilSCAR iteratively applies to every user to detect Sybils. We compare SybilSCAR with state-of-the-art RW-based and LBP-based methods theoretically and empirically. Theoretically, we show that, with proper parameter settings, SybilSCAR has a tighter asymptotical bound on the number of Sybils that are falsely accepted into a social network than existing structure-based methods. Empirically, we perform evaluation using both social networks with synthesized Sybils and a large-scale Twitter dataset (41.7M nodes and 1.2B edges) with real Sybils. Our results show that 1) SybilSCAR is substantially more accurate and more robust to label noise than state-of-the-art RW-based methods; 2) SybilSCAR is more accurate and one order of magnitude more scalable than state-of-the-art LBP-based methods.

Connected through hyperedge HE-1 (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"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "sybilfuse combining local attributes with global structure to perform robust sybil detection"

Connected through hyperedge HE-2 (induced by source paper "graph based security and privacy analytics via collective classification with joint weight learning and propagation"):
  with members:
    - "sybilfuse combining local attributes with global structure to perform robust sybil detection"
    - "smoke screener or straight shooter detecting elite sybil attacks in user review social networks"
    - "node2vec scalable feature learning for networks"
    - "you are who you know and how you behave attribute inference attacks via users social friends and behaviors"
    - "deepwalk online learning of social representations"
    - "detection of early stage enterprise infection by mining large scale log data"
    - "sybilbelief a semi supervised learning approach for structure based sybil detection"
    - "linearized and single pass belief propagation"

Connected through hyperedge HE-3 (induced by source paper "poisoning attacks to graph based recommender systems"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "attriguard a practical defense against attribute inference attacks via adversarial machine learning"
    - "neural collaborative filtering"
    - "a graph based push service platform"
    - "you are who you know and how you behave attribute inference attacks via users social friends and behaviors"
    - "data poisoning attacks on factorization based collaborative filtering"
    - "sybilbelief a semi supervised learning approach for structure based sybil detection"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-4 (induced by source paper "sybilblind detecting fake users in online social networks without manual labels"):
  with members:
    - "sybilfuse combining local attributes with global structure to perform robust sybil detection"
    - "sybilbelief a semi supervised learning approach for structure based sybil detection"
    - "social turing tests crowdsourcing sybil detection"
    - "uncovering social network sybils in the wild"

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)