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: "online supervised learning for traffic load prediction in framed aloha networks"
  Abstract: Predicting the current backlog, or traffic load, in framed-ALOHA networks enables the optimization of resource allocation, e.g., of the frame size. However, this prediction is made difficult by the lack of information about the cardinality of collisions and by possibly complex packet generation statistics. Assuming no prior information about the traffic model, apart from a bound on its temporal memory, this paper develops an online learning-based adaptive traffic load prediction method that is based on Recurrent Neural Networks (RNN) and specifically on the Long Short-Term Memory (LSTM) architecture. In order to enable online training in the absence of feedback on the exact cardinality of collisions, the proposed strategy leverages a novel approximate labeling technique that is inspired by Method of Moments (MOM) estimators. Numerical results show that the proposed online predictor considerably outperforms conventional methods and is able to adapt to changing traffic statistics.

Connected through hyperedge HE-1 (induced by source paper "traffic prediction and random access control optimization learning and non learning based approaches"):
  with members:
    - "artificial intelligence paradigm for customer experience management in next generation networks challenges and perspectives"
    - "fast adaptive s aloha scheme for event driven machine to machine communications"

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)