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: "distributed bayesian detection in the presence of byzantine data"
  Abstract: In this paper, we consider the problem of distributed Bayesian detection in the presence of Byzantines in the network. It is assumed that a fraction of the nodes in the network are compromised and reprogrammed by an adversary to transmit false information to the fusion center (FC) to degrade detection performance. The problem of distributed detection is formulated as a binary hypothesis test at the FC based on 1-bit data sent by the sensors. The expression for minimum attacking power required by the Byzantines to blind the FC is obtained. More specifically, we show that above a certain fraction of Byzantine attackers in the network, the detection scheme becomes completely incapable of utilizing the sensor data for detection. We analyze the problem under different attacking scenarios and derive results for different non-asymptotic cases. It is found that existing asymptotics-based results do not hold under several non-asymptotic scenarios. When the fraction of Byzantines is not sufficient to blind the FC, we also provide closed form expressions for the optimal attacking strategies for the Byzantines that most degrade the detection performance.

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"
    - "data falsification attacks on consensus based detection systems"

Connected through hyperedge HE-2 (induced by source paper "asymptotically optimal stochastic encryption for quantized sequential detection in the presence of eavesdroppers"):
  with members:
    - "distributed inference in the presence of eavesdroppers a survey"
    - "asymptotically optimal anomaly detection via sequential testing"

Connected through hyperedge HE-3 (induced by source paper "byzantine attack and defense in cognitive radio networks a survey"):
  with members:
    - "data falsification attacks on consensus based detection systems"
    - "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"

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.IT(Information Theory)