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: "crowdinside automatic construction of indoor floorplans"
  Abstract: The existence of a worldwide indoor floorplans database can lead to significant growth in location-based applications, especially for indoor environments. In this paper, we present CrowdInside: a crowdsourcing-based system for the automatic construction of buildings floorplans. CrowdInside leverages the smart phones sensors that are ubiquitously available with humans who use a building to automatically and transparently construct accurate motion traces. These accurate traces are generated based on a novel technique for reducing the errors in the inertial motion traces by using the points of interest in the indoor environment, such as elevators and stairs, for error resetting. The collected traces are then processed to detect the overall floorplan shape as well as higher level semantics such as detecting rooms and corridors shapes along with a variety of points of interest in the environment. Implementation of the system in two testbeds, using different Android phones, shows that CrowdInside can detect the points of interest accurately with 0.2% false positive rate and 1.3% false negative rate. In addition, the proposed error resetting technique leads to more than 12 times enhancement in the median distance error compared to the state-of-the-art. Moreover, the detailed floorplan can be accurately estimated with a a relatively small number of traces. This number is amortized over the number of users of the building. We also discuss possible extensions to CrowdInside for inferring even higher level semantics about the discovered floorplans.

Connected through hyperedge HE-1 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans"):
  with members:
    - "teaser fast and certifiable point cloud registration"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "panoptic segmentation"
    - "in perfect shape certifiably optimal 3d shape reconstruction from 2d landmarks"
    - "semi supervised semantic mapping through label propagation with semantic texture meshes"
    - "kimera an open source library for real time metric semantic localization and mapping"
    - "3 d scene graph a sparse and semantic representation of physical environments for intelligent agents"
    - "value iteration networks on multiple levels of abstraction"

Connected through hyperedge HE-2 (induced by source paper "zero calibration device free localization for the iot based on participatory sensing"):
  with members:
    - "transitlabel a crowd sensing system for automatic labeling of transit stations semantics"
    - "a robust zero calibration rf based localization system for realistic environments"
    - "dejavu an accurate energy efficient outdoor localization system"
    - "aroma automatic generation of radio maps for localization systems"

Connected through hyperedge HE-3 (induced by source paper "identifying indoor points of interest via mobile crowdsensing an experimental study"):
  with members:
    - "crowd sensing simultaneous localization and radio fingerprint mapping based on probabilistic similarity models"
    - "extracting point of interest and classifying environment for low sampling crowd sensing smartphone sensor data"

Connected through hyperedge HE-4 (induced by source paper "sensegen a deep learning architecture for synthetic sensor data generation"):
  with members:
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "nips 2016 tutorial generative adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "improved techniques for training gans"
    - "generative adversarial text to image synthesis"
    - "generating sentences from a continuous space"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "gated feedback recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "inferring room semantics using acoustic monitoring"):
  with members:
    - "acoustic scene classification classifying environments from the sounds they produce"

Connected through hyperedge HE-6 (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"
    - "ubibreathe a ubiquitous non invasive wifi based breathing estimator"
    - "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"
    - "a hidden markov model for localization using low end gsm cell phones"

Connected through hyperedge HE-7 (induced by source paper "a robust zero calibration rf based localization system for realistic environments"):
  with members:
    - "transitlabel a crowd sensing system for automatic labeling of transit stations semantics"
    - "lanequest an accurate and energy efficient lane detection system"
    - "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"
    - "a hidden markov model for localization using low end gsm cell phones"

Connected through hyperedge HE-8 (induced by source paper "wigest a ubiquitous wifi based gesture recognition system"):
  with members:
    - "spot an accurate and efficient multi entity device free wlan localization system"
    - "rasid a robust wlan device free passive motion detection system"

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)