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: "sybilbelief a semi supervised learning approach for structure based sybil detection"
  Abstract: Sybil attacks are a fundamental threat to the security of distributed systems. Recently, there has been a growing interest in leveraging social networks to mitigate Sybil attacks. However, the existing approaches suffer from one or more drawbacks, including bootstrapping from either only known benign or known Sybil nodes, failing to tolerate noise in their prior knowledge about known benign or Sybil nodes, and not being scalable. In this paper, we aim to overcome these drawbacks. Toward this goal, we introduce SybilBelief, a semi-supervised learning framework, to detect Sybil nodes. SybilBelief takes a social network of the nodes in the system, a small set of known benign nodes, and, optionally, a small set of known Sybils as input. Then, SybilBelief propagates the label information from the known benign and/or Sybil nodes to the remaining nodes in the system. We evaluate SybilBelief using both synthetic and real-world social network topologies. We show that SybilBelief is able to accurately identify Sybil nodes with low false positive rates and low false negative rates. SybilBelief is resilient to noise in our prior knowledge about known benign and Sybil nodes. Moreover, SybilBelief performs orders of magnitudes better than existing Sybil classification mechanisms and significantly better than existing Sybil ranking mechanisms.

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"

Connected through hyperedge HE-4 (induced by source paper "data poisoning attacks to local differential privacy protocols"):
  with members:
    - "consistent and accurate frequency oracles under local differential privacy"
    - "manipulation attacks in local differential privacy"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "collecting telemetry data privately"
    - "online human bot interactions detection estimation and characterization"
    - "calibrating data to sensitivity in private data analysis"
    - "poisoning attacks against support vector machines"

Connected through hyperedge HE-5 (induced by source paper "graph based security and privacy analytics via collective classification with joint weight learning and propagation"):
  with members:
    - "structure based sybil detection in social networks via local rule based propagation"
    - "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"
    - "linearized and single pass belief propagation"

Connected through hyperedge HE-6 (induced by source paper "poisoning attacks to graph based recommender systems"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "structure based sybil detection in social networks via local rule based propagation"
    - "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"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-7 (induced by source paper "catching loosely synchronized behavior in face of camouflage"):
  with members:
    - "fairjudge trustworthy user prediction in rating platforms"
    - "birdnest bayesian inference for ratings fraud detection"
    - "towards detecting compromised accounts on social networks"
    - "edgecentric anomaly detection in edge attributed networks"
    - "spotting suspicious link behavior with fbox an adversarial perspective"
    - "community detection via semi synchronous label propagation algorithms"
    - "finding deceptive opinion spam by any stretch of the imagination"

Connected through hyperedge HE-8 (induced by source paper "structure based sybil detection in social networks via local rule based propagation"):
  with members:
    - "sybilfuse combining local attributes with global structure to perform robust sybil detection"
    - "truetop a sybil resilient system for user influence measurement on twitter"
    - "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)