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: "ubibreathe a ubiquitous non invasive wifi based breathing estimator"
  Abstract: Monitoring breathing rates and patterns helps in the diagnosis and potential avoidance of various health problems. Current solutions for respiratory monitoring, however, are usually invasive and/or limited to medical facilities. In this paper, we propose a novel respiratory monitoring system, UbiBreathe, based on ubiquitous off-the-shelf WiFi-enabled devices. Our experiments show that the received signal strength (RSS) at a WiFi-enabled device held on a person's chest is affected by the breathing process. This effect extends to scenarios when the person is situated on the line-of-sight (LOS) between the access point and the device, even without holding it. UbiBreathe leverages these changes in the WiFi RSS patterns to enable ubiquitous non-invasive respiratory rate estimation, as well as apnea detection. #R##N#We propose the full architecture and design for UbiBreathe, incorporating various modules that help reliably extract the hidden breathing signal from a noisy WiFi RSS. The system handles various challenges such as noise elimination, interfering humans, sudden user movements, as well as detecting abnormal breathing situations. Our implementation of UbiBreathe using off-the-shelf devices in a wide range of environmental conditions shows that it can estimate different breathing rates with less than 1 breaths per minute (bpm) error. In addition, UbiBreathe can detect apnea with more than 96% accuracy in both the device-on-chest and hands-free scenarios. This highlights its suitability for a new class of anywhere respiratory monitoring.

Connected through hyperedge HE-1 (induced by source paper "besense leveraging wifi channel data and computational intelligence for behavior"):
  with members:
    - "deep learning based nlos identification with commodity wlan devices"

Connected through hyperedge HE-2 (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"
    - "tensorbeat tensor decomposition for monitoring multi person breathing beats with commodity wifi"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "on realistic target coverage by autonomous drones"):
  with members:
    - "design challenges of multi uav systems in cyber physical applications a comprehensive survey and future directions"
    - "the art gallery problem is exists mathbb r complete"
    - "obstacle avoidance strategy using onboard stereo vision on a flapping wing mav"
    - "directing cinematographic drones"
    - "towards a drone cinematographer guiding quadrotor cameras using visual composition principles"
    - "wigest a ubiquitous wifi based gesture recognition system"
    - "context aware computing for the internet of things a survey"

Connected through hyperedge HE-4 (induced by source paper "tensorbeat tensor decomposition for monitoring multi person breathing beats with commodity wifi"):
  with members:
    - "csi based fingerprinting for indoor localization a deep learning approach"

Connected through hyperedge HE-5 (induced by source paper "transitlabel a crowd sensing system for automatic labeling of transit stations semantics"):
  with members:
    - "a robust zero calibration rf based localization system for realistic environments"
    - "the diversity and scale matter ubiquitous transportation mode detection using single cell tower information"
    - "map a crowd sensing system for automatic map semantics identification"
    - "it s the human that matters accurate user orientation estimation for mobile computing applications"
    - "dejavu an accurate energy efficient outdoor localization system"
    - "crowdinside automatic construction of indoor floorplans"
    - "a hidden markov model for localization using low end gsm cell phones"

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.OH(Other Computer Science)