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: "to go or not to go a near unsupervised learning approach for robot navigation"
  Abstract: It is important for robots to be able to decide whether they can go through a space or not, as they navigate through a dynamic environment. This capability can help them avoid injury or serious damage, e.g., as a result of running into people and obstacles, getting stuck, or falling off an edge. To this end, we propose an unsupervised and a near-unsupervised method based on Generative Adversarial Networks (GAN) to classify scenarios as traversable or not based on visual data. Our method is inspired by the recent success of data-driven approaches on computer vision problems and anomaly detection, and reduces the need for vast amounts of negative examples at training time. Collecting negative data indicating that a robot should not go through a space is typically hard and dangerous because of collisions, whereas collecting positive data can be automated and done safely based on the robot's own traveling experience. We verify the generality and effectiveness of the proposed approach on a test dataset collected in a previously unseen environment with a mobile robot. Furthermore, we show that our method can be used to build costmaps (we call as "GoNoGo" costmaps) for robot path planning using visual data only.

Connected through hyperedge HE-1 (induced by source paper "entropic gans meet vaes a statistical approach to compute sample likelihoods in gans"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "solving approximate wasserstein gans to stationarity"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "on unifying deep generative models"
    - "improved training of wasserstein gans"
    - "generate to adapt aligning domains using generative adversarial networks"
    - "learning a driving simulator"

Connected through hyperedge HE-2 (induced by source paper "gonet a semi supervised deep learning approach for traversability estimation"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "learning to fly by crashing"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "nips 2016 tutorial generative adversarial networks"
    - "semantic image inpainting with deep generative models"
    - "robust monocular flight in cluttered outdoor environments"
    - "generative visual manipulation on the natural image manifold"

Connected through hyperedge HE-3 (induced by source paper "path planning in support of smart mobility applications using generative adversarial networks"):
  with members:
    - "enabling cognitive smart cities using big data and machine learning approaches and challenges"
    - "sensegen a deep learning architecture for synthetic sensor data generation"
    - "deep learning for iot big data and streaming analytics a survey"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "improved techniques for training gans"
    - "generative adversarial text to image synthesis"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "car net clairvoyant attentive recurrent network"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "vunet dynamic scene view synthesis for traversability estimation using an rgb camera"
    - "gonet a semi supervised deep learning approach for traversability estimation"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "diversified visual attention networks for fine grained object classification"
    - "image captioning with semantic attention"
    - "recurrent attention models for depth based person identification"

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)