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: "in network outlier detection in wireless sensor networks"
  Abstract: To address the problem of unsupervised outlier detection in wireless sensor networks, we develop an approach that (1) is flexible with respect to the outlier definition, (2) computes the result in-network to reduce both bandwidth and energy consumption, (3) uses only single-hop communication, thus permitting very simple node failure detection and message reliability assurance mechanisms (e.g., carrier-sense), and (4) seamlessly accommodates dynamic updates to data. We examine performance by simulation, using real sensor data streams. Our results demonstrate that our approach is accurate and imposes reasonable communication and power consumption demands.

Connected through hyperedge HE-1 (induced by source paper "machine learning in iot security current solutions and future challenges"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "machine learning ddos detection for consumer internet of things devices"
    - "ai based two stage intrusion detection for software defined iot networks"
    - "machine intelligence techniques for next generation context aware wireless networks"
    - "securing the internet of things in the age of machine learning and software defined networking"
    - "semisupervised deep reinforcement learning in support of iot and smart city services"
    - "applications of deep learning and reinforcement learning to biological data"
    - "deep learning for iot big data and streaming analytics a survey"

Connected through hyperedge HE-2 (induced by source paper "sadan scalable adversary detection in autonomous networks"):
  with members:
    - "scalable byzantine consensus via hardware assisted secret sharing"
    - "wise lightweight intelligent swarm attestation scheme for iot the verifier s perspective"
    - "a survey on low latency towards 5g ran core network and caching solutions"
    - "c flat control flow attestation for embedded systems software"
    - "enhancing bitcoin security and performance with strong consistency via collective signing"
    - "exploiting social navigation"
    - "code injection attacks on harvard architecture devices"

Connected through hyperedge HE-3 (induced by source paper "iot security techniques based on machine learning"):
  with members:
    - "mobile edge computing fog et al a survey and analysis of security threats and challenges"
    - "machine learning methods for attack detection in the smart grid"
    - "machine learning in wireless sensor networks algorithms strategies and applications"

Connected through hyperedge HE-4 (induced by source paper "machine learning in wireless sensor networks algorithms strategies and applications"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "exact soft confidence weighted learning"
    - "structured compressed sensing from theory to applications"
    - "compressive sensing over networks"
    - "distributed learning in wireless sensor networks"

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.DB(Databases)