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: "position tracking for virtual reality using commodity wifi"
  Abstract: Today, experiencing virtual reality (VR) is a cumbersome experience which either requires dedicated infrastructure like infrared cameras to track the headset and hand-motion controllers (e.g., Oculus Rift, HTC Vive), or provides only 3-DoF (Degrees of Freedom) tracking which severely limits the user experience (e.g., Samsung Gear). To truly enable VR everywhere, we need position tracking to be available as a ubiquitous service. This paper presents WiCapture, a novel approach which leverages commodity WiFi infrastructure, which is ubiquitous today, for tracking purposes. We prototype WiCapture using off-the-shelf WiFi radios and show that it achieves an accuracy of 0.88 cm compared to sophisticated infrared based tracking systems like the Oculus, while providing much higher range, resistance to occlusion, ubiquity and ease of deployment.

Connected through hyperedge HE-1 (induced by source paper "et tu alexa when commodity wifi devices turn into adversarial motion sensors"):
  with members:
    - "peek a boo i see your smart home activities even encrypted"
    - "spying on the smart home privacy attacks and defenses on encrypted iot traffic"
    - "a study of mac address randomization in mobile devices and when it fails"
    - "wi fi gesture recognition on existing devices"

Connected through hyperedge HE-2 (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"
    - "tensorbeat tensor decomposition for monitoring multi person breathing beats with 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-3 (induced by source paper "can wifi estimate person pose"):
  with members:
    - "csi net unified human body characterization and action recognition"
    - "yolov3 an incremental improvement"
    - "cascaded pyramid network for multi person pose estimation"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "rmpe regional multi person pose estimation"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "on vr spatial query for dual entangled worlds"):
  with members:
    - "communication computing and caching for mobile vr delivery modeling and trade off"
    - "k nearest neighbors on road networks a journey in experimentation and in memory implementation"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"

Connected through hyperedge HE-5 (induced by source paper "adversarial wifi sensing using a single smartphone"):
  with members:
    - "a study of mac address randomization in mobile devices and when it fails"
    - "back to the drawing board revisiting the design of optimal location privacy preserving mechanisms"
    - "powerspy location tracking using mobile device power analysis"
    - "protecting locations with differential privacy under temporal correlations"
    - "optimal geo indistinguishable mechanisms for location privacy"

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.CV(Computer Vision and Pattern Recognition)