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: "a survey of multi objective optimization in wireless sensor networks metrics algorithms and open problems"
  Abstract: Wireless sensor networks (WSNs) have attracted substantial research interest, especially in the context of performing monitoring and surveillance tasks. However, it is challenging to strike compelling tradeoffs amongst the various conflicting optimization criteria, such as the network’s energy dissipation, packet-loss rate, coverage, and lifetime. This paper provides a tutorial and survey of recent research and development efforts addressing this issue by using the technique of multi-objective optimization (MOO). First, we provide an overview of the main optimization objectives used in WSNs. Then, we elaborate on various prevalent approaches conceived for MOO, such as the family of mathematical programming-based scalarization methods, the family of heuristics/metaheuristics-based optimization algorithms, and a variety of other advanced optimization techniques. Furthermore, we summarize a range of recent studies of MOO in the context of WSNs, which are intended to provide useful guidelines for researchers to understand the referenced literature. Finally, we discuss a range of open problems to be tackled by future research.

Connected through hyperedge HE-1 (induced by source paper "thirty years of machine learning the road to pareto optimal next generation wireless networks"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "an introduction to deep learning for the physical layer"
    - "budget constrained multi armed bandits with multiple plays"
    - "unsupervised machine learning for networking techniques applications and research challenges"
    - "recent trends in deep learning based natural language processing"
    - "detection algorithms for communication systems using deep learning"

Connected through hyperedge HE-2 (induced by source paper "big data analytics for large scale wireless networks challenges and opportunities"):
  with members:
    - "blockchain for internet of things a survey"
    - "big data analytics for manufacturing internet of things opportunities challenges and enabling technologies"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "stealing hyperparameters in machine learning"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "mobile edge computing a survey on architecture and computation offloading"

Connected through hyperedge HE-3 (induced by source paper "aeronautical ad hoc networking for the internet above the clouds"):
  with members:
    - "optimizing space air ground integrated networks by artificial intelligence"
    - "regularized zero forcing precoding aided adaptive coding and modulation for large scale antenna array based air to air communications"
    - "optical communication in space challenges and mitigation techniques"
    - "adaptive coding and modulation for large scale antenna array based aeronautical communications in the presence of co channel interference"
    - "survey of important issues in uav communication networks"
    - "on the security of the automatic dependent surveillance broadcast protocol"
    - "security challenges in mobile ad hoc networks a survey"
    - "massive mimo for next generation wireless systems"

Connected through hyperedge HE-4 (induced by source paper "maximum lifetime convergecast tree in wireless sensor networks"):
  with members:
    - "design and implementation of a wireless sensor network for agricultural applications"
    - "power aware routing for sensor databases"

Connected through hyperedge HE-5 (induced by source paper "low complexity and high resolution doa estimation for hybrid analog and digital massive mimo receive array"):
  with members:
    - "robust secure transmission of using main lobe integration based leakage beamforming in directional modulation mu mimo systems"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "energy efficient uav communication with trajectory optimization"
    - "artificial noise aided secure transmission with directional modulation based on random frequency diverse arrays"
    - "spectral efficiency of mixed adc receivers for massive mimo systems"
    - "directional modulation via symbol level precoding a way to enhance security"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "spatially sparse precoding in millimeter wave mimo systems"

Connected through hyperedge HE-6 (induced by source paper "energy efficient multi pair two way af full duplex massive mimo relaying"):
  with members:
    - "power allocation for multi pair massive mimo two way af relaying with linear processing"
    - "joint beamforming optimization and power control for full duplex mimo two way relay channel"
    - "multipair full duplex relaying with massive arrays and linear processing"
    - "energy efficiency optimization in relay assisted mimo systems with perfect and statistical csi"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "in band full duplex wireless challenges and opportunities"
    - "green cellular networks a survey some research issues and challenges"
    - "energy and spectral efficiency of very large multiuser mimo systems"

Connected through hyperedge HE-7 (induced by source paper "secure directional modulation to enhance physical layer security in iot networks"):
  with members:
    - "robust secure transmission of using main lobe integration based leakage beamforming in directional modulation mu mimo systems"
    - "artificial noise aided secure transmission in wiretap channels with transmitter side correlation"
    - "directional modulation via symbol level precoding a way to enhance security"
    - "relay selection improves the security reliability trade off in cognitive radio systems"

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)