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: "look ahead before you leap end to end active recognition by forecasting the effect of motion"
  Abstract: Visual recognition systems mounted on autonomous moving agents face the challenge of unconstrained data, but simultaneously have the opportunity to improve their performance by moving to acquire new views of test data. In this work, we first show how a recurrent neural network-based system may be trained to perform end-to-end learning of motion policies suited for this "active recognition" setting. Further, we hypothesize that active vision requires an agent to have the capacity to reason about the effects of its motions on its view of the world. To verify this hypothesis, we attempt to induce this capacity in our active recognition pipeline, by simultaneously learning to forecast the effects of the agent's motions on its internal representation of the environment conditional on all past views. Results across two challenging datasets confirm both that our end-to-end system successfully learns meaningful policies for active category recognition, and that "learning to look ahead" further boosts recognition performance.

Connected through hyperedge HE-1 (induced by source paper "emergence of exploratory look around behaviors through active observation completion"):
  with members:
    - "learning exploration policies for navigation"
    - "learning real world robot policies by dreaming"
    - "sidekick policy learning for active visual exploration"
    - "semi parametric topological memory for navigation"
    - "world models"
    - "learn to score efficient 3d scene exploration by predicting view utility"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "building generalizable agents with a realistic and rich 3d environment"

Connected through hyperedge HE-2 (induced by source paper "a deep recurrent q network towards self adapting distributed microservices architecture"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "continuous control with deep reinforcement learning"
    - "dueling network architectures for deep reinforcement learning"
    - "deep recurrent q learning for partially observable mdps"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-3 (induced by source paper "sidekick policy learning for active visual exploration"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "unifying map and landmark based representations for visual navigation"
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "im2pano3d extrapolating 360 structure and semantics beyond the field of view"
    - "carla an open urban driving simulator"
    - "a dataset for developing and benchmarking active vision"
    - "visualizing and understanding atari agents"
    - "asymmetric actor critic for image based robot learning"

Connected through hyperedge HE-4 (induced by source paper "geometry aware recurrent neural networks for active visual recognition"):
  with members:
    - "3d interpreter networks for viewer centered wireframe modeling"
    - "cycada cycle consistent adversarial domain adaptation"
    - "learning a multi view stereo machine"
    - "a dataset for developing and benchmarking active vision"
    - "unsupervised learning of depth and ego motion from video"
    - "neural map structured memory for deep reinforcement learning"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "marrnet 3d shape reconstruction via 2 5d sketches"

Connected through hyperedge HE-5 (induced by source paper "snap angle prediction for 360 circ panorama"):
  with members:
    - "pixel objectness learning to segment generic objects automatically in images and videos"
    - "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"
    - "action search spotting actions in videos and its application to temporal action localization"
    - "making 360 circ video watchable in 2d learning videography for click free viewing"
    - "learning to look around intelligently exploring unseen environments for unknown tasks"
    - "leaving some stones unturned dynamic feature prioritization for activity detection in streaming video"
    - "pano2vid automatic cinematography for watching 360 circ videos"
    - "photo aesthetics ranking network with attributes and content adaptation"

Connected through hyperedge HE-6 (induced by source paper "hierarchical long term video prediction without supervision"):
  with members:
    - "stochastic video generation with a learned prior"
    - "decomposing motion and content for natural video sequence prediction"
    - "learning to generate long term future via hierarchical prediction"
    - "stochastic variational video prediction"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "unsupervised learning for physical interaction through video prediction"
    - "video pixel networks"
    - "unsupervised learning of video representations using lstms"

Connected through hyperedge HE-7 (induced by source paper "attend before you act leveraging human visual attention for continual learning"):
  with members:
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "shapecodes self supervised feature learning by lifting views to viewgrids"):
  with members:
    - "learning pose specific representations by predicting different views"
    - "deep view morphing"
    - "transitive invariance for self supervised visual representation learning"
    - "im2pano3d extrapolating 360 structure and semantics beyond the field of view"
    - "colorization as a proxy task for visual understanding"
    - "learning to look around intelligently exploring unseen environments for unknown tasks"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "learning a predictable and generative vector representation for objects"

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)