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: "data falsification attacks on consensus based detection systems"
  Abstract: This paper considers the problem of signal detection in distributed networks in the presence of data falsification (Byzantine) attacks. Detection approaches considered in the paper are based on fully distributed consensus algorithms, where all of the nodes exchange information only with their neighbors in the absence of a fusion center. For such networks, we first characterize the negative effect of Byzantines on the steady state and transient detection performance of conventional consensus-based detection algorithms. To avoid performance deterioration, we propose a distributed weighted average consensus algorithm that is robust to Byzantine attacks. We show that, under reasonable assumptions, the global test statistic for detection can be computed locally at each node using our proposed consensus algorithm. We exploit the statistical distribution of the nodes’ data to devise techniques for mitigating the influence of data falsifying Byzantines on the distributed detection system. Since some parameters of the statistical distribution of the nodes’ data might not be known  a priori , we propose learning based techniques to enable an adaptive design of the local fusion or update rules. Numerical results are presented for illustration.

Connected through hyperedge HE-1 (induced by source paper "the internet of things secure distributed inference"):
  with members:
    - "byzantine resilient locally optimum detection using collaborative autonomous networks"
    - "byrdie byzantine resilient distributed coordinate descent for decentralized learning"
    - "distributed bayesian detection in the presence of byzantine data"

Connected through hyperedge HE-2 (induced by source paper "byzantine resilient locally optimum detection using collaborative autonomous networks"):
  with members:
    - "topology for distributed inference on graphs"

Connected through hyperedge HE-3 (induced by source paper "social learning over weakly connected graphs"):
  with members:
    - "information exchange and learning dynamics over weakly connected adaptive networks"
    - "interactive sensing and decision making in social networks"
    - "higher dimensional consensus learning in large scale networks"

Connected through hyperedge HE-4 (induced by source paper "robust consensus based network intrusion detection in presence of byzantine attacks"):
  with members:
    - "a consensus based network intrusion detection system"

Connected through hyperedge HE-5 (induced by source paper "byzantine attack and defense in cognitive radio networks a survey"):
  with members:
    - "distributed bayesian detection in the presence of byzantine data"
    - "physical layer security with multiuser scheduling in cognitive radio networks"
    - "attack prevention for collaborative spectrum sensing in cognitive radio networks"
    - "robust beamforming for security in mimo wiretap channels with imperfect csi"
    - "on the relationship between the multi antenna secrecy communications and cognitive radio communications"
    - "the secrecy capacity of the mimo wiretap channel"

Connected through hyperedge HE-6 (induced by source paper "fault tolerant multi agent optimization part iii"):
  with members:
    - "byzantine multi agent optimization part ii"
    - "matrix representation of iterative approximate byzantine consensus in directed graphs"
    - "iterative approximate byzantine consensus in arbitrary directed graphs"

Connected through hyperedge HE-7 (induced by source paper "byzantine multi agent optimization part ii"):
  with members:
    - "consensus of multi agent networks in the presence of adversaries using only local information"
    - "matrix representation of iterative approximate byzantine consensus in directed graphs"
    - "iterative approximate byzantine consensus in arbitrary directed graphs"

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.SY(Systems and Control)