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: "proactive received power prediction using machine learning and depth images for mmwave networks"
  Abstract: This study demonstrates the feasibility of proactive received power prediction by leveraging spatiotemporal visual sensing information towards reliable millimeter-wave (mmWave) networks. As the received power on a mmWave link can attenuate aperiodically owing to human blockages, a long-term series of the future received power cannot be predicted by analyzing the received signals prior to the blockage occurring. We propose a novel mechanism that predicts the time series of received power from the next moment to as many as several hundred milliseconds ahead. The key idea is to leverage camera imagery and machine learning (ML). Time-sequential images may involve the spatial geometry and mobility of obstacles representing mmWave signal propagation. ML is used to construct a prediction model from a dataset of sequential images labeled with received power in several hundred milliseconds ahead of the time at which each image is obtained. The simulation and experimental evaluations conducted using IEEE 802.11ad devices and a depth camera demonstrated that the proposed mechanism employing convolutional long short-term memory predicted a time series of received power up to 500 ms ahead, with an inference time of less than 3 ms and a root-mean-square error of 3.4 dB.

Connected through hyperedge HE-1 (induced by source paper "communication efficient multimodal split learning for mmwave received power prediction"):
  with members:
    - "distributed learning of deep neural network over multiple agents"
    - "an mdp model for optimal handover decisions in mmwave cellular networks"
    - "a three state received signal strength model for device free localization"

Connected through hyperedge HE-2 (induced by source paper "end to end learning of proactive handover policy for camera assisted mmwave networks using deep reinforcement learning"):
  with members:
    - "improved handover through dual connectivity in 5g mmwave mobile networks"
    - "deep reinforcement learning with double q learning"
    - "an mdp model for optimal handover decisions in mmwave cellular networks"
    - "depth map prediction from a single image using a multi scale deep network"

Connected through hyperedge HE-3 (induced by source paper "one pixel image and rf signal based split learning for mmwave received power prediction"):
  with members:
    - "minimizing latency to support vr social interactions over wireless cellular systems via bandwidth allocation"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "tractable resource management with uplink decoupled millimeter wave overlay in ultra dense cellular networks"

Connected through hyperedge HE-4 (induced by source paper "veni vidi dixi reliable wireless communication with depth images"):
  with members:
    - "deep complex networks"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "channel sounding for the masses low complexity gnu 802 11b channel impulse response estimation"

Connected through hyperedge HE-5 (induced by source paper "a very brief introduction to machine learning with applications to communication systems"):
  with members:
    - "end to end deep learning of optical fiber communications"
    - "deepcode feedback codes via deep learning"
    - "machine learning at the edge a data driven architecture with applications to 5g cellular networks"
    - "online label recovery for deep learning based communication through error correcting codes"
    - "deep joint source channel coding for wireless image transmission"
    - "deep learning in physical layer communications"
    - "deep learning methods for improved decoding of linear codes"
    - "learning the mmse channel estimator"

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)