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: "cream condensed real time models for depth prediction using convolutional neural networks"
  Abstract: Since the resurgence of CNNs the robotic vision community has developed a range of algorithms that perform classification, semantic segmentation and structure prediction (depths, normals, surface curvature) using neural networks. While some of these models achieve state-of-the art results and super human level performance, deploying these models in a time critical robotic environment remains an ongoing challenge. Real-time frameworks are of paramount importance to build a robotic society where humans and robots integrate seamlessly. To this end, we present a novel real-time structure prediction framework that predicts depth at 30fps on an NVIDIA-TX2. At the time of writing, this is the first piece of work to showcase such a capability on a mobile platform. We also demonstrate with extensive experiments that neural networks with very large model capacities can be leveraged in order to train accurate condensed model architectures in a "from teacher to student" style knowledge transfer.

Connected through hyperedge HE-1 (induced by source paper "structured knowledge distillation for dense prediction"):
  with members:
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "fcos fully convolutional one stage object detection"
    - "cornernet detecting objects as paired keypoints"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "deep attention based classification network for robust depth prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "ocnet object context network for scene parsing"

Connected through hyperedge HE-2 (induced by source paper "empnet neural localisation and mapping using embedded memory points"):
  with members:
    - "deeptam deep tracking and mapping"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "just in time reconstruction inpainting sparse maps using single view depth predictors as priors"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "traversing latent space using decision ferns"
    - "eng end to end neural geometry for robust depth and pose estimation using cnns"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"

Connected through hyperedge HE-3 (induced by source paper "enforcing geometric constraints of virtual normal for depth prediction"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "an evaluation of feature matchers forfundamental matrix estimation"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "fcos fully convolutional one stage object detection"
    - "deep attention based classification network for robust depth prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "rethinking monocular depth estimation with adversarial training"
    - "learning a discriminative feature network for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "fast neural architecture search of compact semantic segmentation models via auxiliary cells"):
  with members:
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "taskonomy disentangling task transfer learning"
    - "simple baselines for human pose estimation and tracking"
    - "neural architecture optimization"
    - "light weight refinenet for real time semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "neural architecture search with bayesian optimisation and optimal transport"

Connected through hyperedge HE-5 (induced by source paper "double refinement network for efficient indoor monocular depth estimation"):
  with members:
    - "deep ordinal regression network for monocular depth estimation"
    - "revisiting single image depth estimation toward higher resolution maps with accurate object boundaries"
    - "fast end to end trainable guided filter"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "real time joint semantic segmentation and depth estimation using asymmetric annotations"
    - "monocular depth estimation by learning from heterogeneous datasets"
    - "learning depth from single images with deep neural network embedding focal length"
    - "semi supervised deep learning for monocular depth map prediction"

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)