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: "enabling multi hop ad hoc networks through wifi direct multi group networking"
  Abstract: With the increasing availability of mobile devices that natively support ad hoc communication protocols, we are presented with a unique opportunity to realize large scale ad hoc wireless networks. Recently, a novel ad hoc protocol named WiFi Direct has been proposed and standardized by the WiFi Alliance with the objective of facilitating the interconnection of nearby devices. However, WiFi Direct has been designed following a client-server hierarchical architecture, where a single device manages all the communications within a group of devices. In this paper, we propose and analyze different practical solutions for supporting the communications between multiple WiFi Direct groups using Android OS devices. By describing the WiFi Direct standard and the limitations of the current implementation of the Android WiFi Direct framework, we present possible solutions to interconnect different groups to create multi-hop ad hoc networks. Experimental results show that our proposed approaches are feasible with different overhead in terms of energy consumption and delay at the gateway node. Additionally, our experimental results demonstrate the superiority of techniques that exploit the device ability to maintain simultaneous physical connections to multiple groups, enabling multi-hop ad hoc networks with low overhead.

Connected through hyperedge HE-1 (induced by source paper "deepwifi cognitive wifi with deep learning"):
  with members:
    - "generative adversarial learning for spectrum sensing"
    - "deep learning for launching and mitigating wireless jamming attacks"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "deep sensing cooperative spectrum sensing based on convolutional neural networks"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "convolutional radio modulation recognition networks"
    - "tensorflow a system for large scale machine learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "fair airtime allocation for content dissemination in wifi direct based mobile social networks"):
  with members:
    - "content centric routing in wi fi direct multi group networks"
    - "rigorous and practical proportional fair allocation for multi rate wi fi"

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)