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: "wireless communications in the era of big data"
  Abstract: The rapidly growing wave of wireless data service is pushing against the boundary of our communication network's processing power. The pervasive and exponentially increasing data traffic present imminent challenges to all aspects of wireless system design, such as spectrum efficiency, computing capabilities, and fronthaul/backhaul link capacity. In this article, we discuss the challenges and opportunities in the design of scalable wireless systems to embrace the big data era. On one hand, we review the state-of-the-art networking architectures and signal processing techniques adaptable for managing big data traffic in wireless networks. On the other hand, instead of viewing mobile big data as an unwanted burden, we introduce methods to capitalize on the vast data traffic, for building a big-data-aware wireless network with better wireless service quality and new mobile applications. We highlight several promising future research directions for wireless communications in the mobile big data era.

Connected through hyperedge HE-1 (induced by source paper "artificial intelligence enabled wireless networking for 5g and beyond recent advances and future challenges"):
  with members:
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "low rf complexity technologies to enable millimeter wave mimo with large antenna array for 5g wireless communications"

Connected through hyperedge HE-2 (induced by source paper "importance of small probability events in big data information measures applications and challenges"):
  with members:
    - "local deep feature alignment for unsupervised dimension reduction"
    - "matching users preference under target revenue constraints in data recommendation systems"
    - "differential message importance measure a new approach to the required sampling number in big data structure characterization"
    - "decorrelation of neutral vector variables theory and applications"
    - "group cooperation with optimal resource allocation in wireless powered communication networks"
    - "amplifying inter message distance on information divergence measures in big data"
    - "focusing on a probability element parameter selection of message importance measure in big data"
    - "linear complexity exponentially consistent tests for universal outlying sequence detection"

Connected through hyperedge HE-3 (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"
    - "a survey of multi objective optimization in wireless sensor networks metrics algorithms and open problems"

Connected through hyperedge HE-4 (induced by source paper "load balancing for ultra dense networks a deep reinforcement learning based approach"):
  with members:
    - "applications of deep learning and reinforcement learning to biological data"
    - "ultra dense hetnets meet big data green frameworks techniques and approaches"
    - "asynchronous methods for deep reinforcement learning"
    - "continuous control with deep reinforcement learning"
    - "massively parallel methods for deep reinforcement learning"
    - "distributed policy evaluation under multiple behavior strategies"
    - "a comparative study of efficient initialization methods for the k means clustering algorithm"

Connected through hyperedge HE-5 (induced by source paper "matching users preference under target revenue constraints in data recommendation systems"):
  with members:
    - "information measure similarity theory message importance measure via shannon entropy"
    - "group cooperation with optimal resource allocation in wireless powered communication networks"
    - "focusing on a probability element parameter selection of message importance measure in big data"
    - "message importance measure and its application to minority subset detection in big data"
    - "differentially private online learning for cloud based video recommendation with multimedia big data in social networks"
    - "optimal forgery and suppression of ratings for privacy enhancement in recommendation systems"
    - "renyi divergence and kullback leibler divergence"

Connected through hyperedge HE-6 (induced by source paper "a flexible machine learning aware architecture for future wlans"):
  with members:
    - "komondor a wireless network simulator for next generation high density wlans"
    - "machine learning for networking workflow advances and opportunities"
    - "deep learning in mobile and wireless networking a survey"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "unsupervised machine learning for networking techniques applications and research challenges"

Connected through hyperedge HE-7 (induced by source paper "when machine learning meets big data a wireless communication perspective"):
  with members:
    - "deep learning in physical layer communications"
    - "deep reinforcement learning for online offloading in wireless powered mobile edge computing networks"
    - "big data analytics machine learning and artificial intelligence in next generation wireless networks"

Connected through hyperedge HE-8 (induced by source paper "autonomous wireless systems with artificial intelligence"):
  with members:
    - "a survey of anticipatory mobile networking context based classification prediction methodologies and optimization techniques"

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)