human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "data aggregation techniques for internet of things"):
  Abstract: The goal of this dissertation is to design efficient data aggregation frameworks for massive IoT networks in different scenarios to support the proper functioning of IoT analytics layer. This dissertation includes modern algorithmic frameworks such as non convex optimization, machine learning, stochastic matrix perturbation theory and federated filtering along with modern computing infrastructure such as fog computing and cloud computing. The development of such an ambitious design involves many open challenges, this proposal envisions three major open challenges for IoT data aggregation: first, severe resource constraints of IoT nodes due to limited power and computational ability, second, the highly uncertain (unreliable) raw IoT data is not fit for decisionmaking and third, network latency and privacy issue for critical applications. This dissertation presents three independent novel approaches for distinct scenarios to solve one or more aforementioned open challenges. The first approach focuses on energy efficient routing; discusses a clustering protocol based on device to device communication for both stationary and mobile IoT nodes. The second approach focuses on processing uncertain raw IoT data; presents an IoT data aggregation scheme to improve the quality of raw IoT data. Finally, the third approach focuses on power loss due to communication overhead and privacy issues for medical IoT devices (IoMT); describes a prediction based data aggregation framework for massive IoMT devices.

Includes members:
  Member "a federated filtering framework for internet of medical things":
    also connected through other hyperedges:
      (none)
  Member "on the importance and feasibility of forecasting data in sensors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a federated filtering framework for internet of medical things")
  Member "communication efficient learning of deep networks from decentralized data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "a survey about prediction based data reduction in wireless sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a federated filtering framework for internet of medical things")
  Member "robust pca via outlier pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "robust principal component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")

Question: Please tell me which class the source paper 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)