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: "steps towards a theory of visual information active perception signal to symbol conversion and the interplay between sensing and control"
  Abstract: This manuscript describes the elements of a theory of information tailored to control and decision tasks and specifically to visual data. The concept of Actionable Information is described, that relates to a notion of information championed by J. Gibson, and a notion of "complete information" that relates to the minimal sufficient statistics of a complete representation. It is shown that the "actionable information gap" between the two can be reduced by exercising control on the sensing process. Thus, senging, control and information are inextricably tied. This has consequences in the so-called "signal-to-symbol barrier" problem, as well as in the analysis and design of active sensing systems. It has ramifications in vision-based control, navigation, 3-D reconstruction and rendering, as well as detection, localization, recognition and categorization of objects and scenes in live video. #R##N#This manuscript has been developed from a set of lecture notes for a summer course at the First International Computer Vision Summer School (ICVSS) in Scicli, Italy, in July of 2008. They were later expanded and amended for subsequent lectures in the same School in July 2009. Starting on November 1, 2009, they were further expanded for a special topics course, CS269, taught at UCLA in the Spring term of 2010.

Connected through hyperedge HE-1 (induced by source paper "learning task driven control policies via information bottlenecks"):
  with members:
    - "differentiable algorithm networks for composable robot learning"
    - "learning efficient representation for intrinsic motivation"
    - "on variational bounds of mutual information"
    - "learning representations in reinforcement learning an information bottleneck approach"
    - "unsupervised visuomotor control through distributional planning networks"
    - "the limits and potentials of deep learning for robotics"
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "particle filter networks with application to visual localization"

Connected through hyperedge HE-2 (induced by source paper "symmetry based disentangled representation learning requires interaction with environments"):
  with members:
    - "s trigger continual state representation learning via self triggered generative replay"
    - "are disentangled representations helpful for abstract visual reasoning"
    - "on the fairness of disentangled representations"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "decoupling feature extraction from policy learning assessing benefits of state representation learning in goal based robotics"
    - "flatland a lightweight first person 2 d environment for reinforcement learning"
    - "towards a definition of disentangled representations"
    - "recurrent world models facilitate policy evolution"

Connected through hyperedge HE-3 (induced by source paper "past present and future of simultaneous localization and mapping toward the robust perception age"):
  with members:
    - "on manifold preintegration for real time visual inertial odometry"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "event based 6 dof camera tracking for high speed applications"
    - "monte carlo localization in hand drawn maps"
    - "on manifold preintegration theory for fast and accurate visual inertial navigation"
    - "monocular slam supported object recognition"
    - "lagrangian duality in 3d slam verification techniques and optimal solutions"

Connected through hyperedge HE-4 (induced by source paper "why and when can deep but not shallow networks avoid the curse of dimensionality a review"):
  with members:
    - "deep vs shallow networks an approximation theory perspective"
    - "bridging the gaps between residual learning recurrent neural networks and visual cortex"
    - "depth width tradeoffs in approximating natural functions with neural networks"
    - "the power of depth for feedforward neural networks"
    - "deep convolutional networks are hierarchical kernel machines"
    - "representation benefits of deep feedforward networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "on the expressive power of deep learning a tensor analysis"

Connected through hyperedge HE-5 (induced by source paper "learning functions when is deep better than shallow"):
  with members:
    - "bridging the gaps between residual learning recurrent neural networks and visual cortex"
    - "representation benefits of deep feedforward networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-6 (induced by source paper "perception aware path planning"):
  with members:
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-7 (induced by source paper "domain size pooling in local descriptors dsp sift"):
  with members:
    - "dense correspondences across scenes and scales"
    - "visual scene representations contrast scaling and occlusion"
    - "return of the devil in the details delving deep into convolutional nets"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "descriptor matching with convolutional neural networks a comparison to sift"
    - "on the design and analysis of multiple view descriptors"
    - "unsupervised feature learning by augmenting single images"
    - "scene parsing with multiscale feature learning purity trees and optimal covers"

Connected through hyperedge HE-8 (induced by source paper "transformation properties of learned visual representations"):
  with members:
    - "learning the irreducible representations of commutative lie groups"
    - "learning stable group invariant representations with convolutional networks"
    - "invariant scattering convolution networks"
    - "on multi view feature learning"
    - "an unsupervised algorithm for learning lie group transformations"

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)