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: "dynamic network cartography advances in network health monitoring"
  Abstract: Communication networks have evolved from specialized research and tactical transmission systems to large-scale and highly complex interconnections of intelligent devices, increasingly becoming more commercial, consumer oriented, and heterogeneous. Propelled by emergent social networking services and high-definition streaming platforms, network traffic has grown explosively thanks to the advances in processing speed and storage capacity of state-of-the-art communication technologies. As "netizens" demand a seamless networking experience that entails not only higher speeds but also resilience and robustness to failures and malicious cyberattacks, ample opportunities for signal processing (SP) research arise. The vision is for ubiquitous smart network devices to enable data-driven statistical learning algorithms for distributed, robust, and online network operation and management, adaptable to the dynamically evolving network landscape with minimal need for human intervention. This article aims to delineate the analytical background and the relevance of SP tools to dynamic network monitoring, introducing the SP readership to the concept of dynamic network cartography? a framework to construct maps of the dynamic network state in an efficient and scalable manner tailored to large-scale heterogeneous networks.

Connected through hyperedge HE-1 (induced by source paper "signal processing for high throughput satellites challenges in new interference limited scenarios"):
  with members:
    - "clustering strategies for multicast precoding in multi beam satellite systems"
    - "generalized multicast multibeam precoding for satellite communications"
    - "robust transmission in downlink multiuser miso systems a rate splitting approach"
    - "admm based fast algorithm for multi group multicast beamforming in large scale wireless systems"
    - "resource management in non orthogonal multiple access networks for 5g and beyond"
    - "massive mimo ten myths and one critical question"
    - "hybrid beamforming for massive mimo a survey"
    - "dvb s2x enabled precoding for high throughput satellite systems"

Connected through hyperedge HE-2 (induced by source paper "fusion subspace clustering full and incomplete data"):
  with members:
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust subspace clustering"
    - "guess who rated this movie identifying users through subspace clustering"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "high rank matrix completion and subspace clustering with missing data"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-3 (induced by source paper "sparsity promoting sensor selection for non linear measurement models"):
  with members:
    - "near optimal sensor placement for linear inverse problems"
    - "sparsity exploiting anchor placement for localization in sensor networks"
    - "sensor selection for event detection 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.NI(Networking and Internet Architecture)