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: "tensorbeat tensor decomposition for monitoring multi person breathing beats with commodity wifi"
  Abstract: Breathing signal monitoring can provide important clues for human's physical health problems. Comparing to existing techniques that require wearable devices and special equipment, a more desirable approach is to provide contact-free and long-term breathing rate monitoring by exploiting wireless signals. In this paper, we propose TensorBeat, a system to employ channel state information (CSI) phase difference data to intelligently estimate breathing rates for multiple persons with commodity WiFi devices. The main idea is to leverage the tensor decomposition technique to handle the CSI phase difference data. The proposed TensorBeat scheme first obtains CSI phase difference data between pairs of antennas at the WiFi receiver to create CSI tensor data. Then Canonical Polyadic (CP) decomposition is applied to obtain the desired breathing signals. A stable signal matching algorithm is developed to find the decomposed signal pairs, and a peak detection method is applied to estimate the breathing rates for multiple persons. Our experimental study shows that TensorBeat can achieve high accuracy under different environments for multi-person breathing rate monitoring.

Connected through hyperedge HE-1 (induced by source paper "joint activity recognition and indoor localization with wifi fingerprints"):
  with members:
    - "continuous user authentication by contactless wireless sensing"
    - "csi net unified human body characterization and action recognition"
    - "embedding structured contour and location prior in siamesed fully convolutional networks for road detection"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "position tracking for virtual reality using commodity wifi"
    - "mask r cnn"
    - "squeezeseg convolutional neural nets with recurrent crf for real time road object segmentation from 3d lidar point cloud"

Connected through hyperedge HE-2 (induced by source paper "wi fi sensing applications and challenges"):
  with members:
    - "privacy aware smart metering progress and challenges"
    - "low latency heterogeneous networks with millimeter wave communications"
    - "csi based fingerprinting for indoor localization a deep learning approach"
    - "monitoring breathing via signal strength in wireless networks"
    - "through wall tracking using variance based radio tomography networks"
    - "novelty and collective attention"

Connected through hyperedge HE-3 (induced by source paper "csi net unified human body characterization and action recognition"):
  with members:
    - "an analysis of deep neural network models for practical applications"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "the kinetics human action video dataset"
    - "semi supervised learning with context conditional generative adversarial networks"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "mask r cnn"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "improved techniques for training gans"

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.CE(Computational Engineering, Finance, and Science)