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: "leaving some stones unturned dynamic feature prioritization for activity detection in streaming video"
  Abstract: Current approaches for activity recognition often ignore constraints on computational resources: 1) they rely on extensive feature computation to obtain rich descriptors on all frames, and 2) they assume batch-mode access to the entire test video at once. We propose a new active approach to activity recognition that prioritizes "what to compute when" in order to make timely predictions. The main idea is to learn a policy that dynamically schedules the sequence of features to compute on selected frames of a given test video. In contrast to traditional static feature selection, our approach continually re-prioritizes computation based on the accumulated history of observations and accounts for the transience of those observations in ongoing video. We develop variants to handle both the batch and streaming settings. On two challenging datasets, our method provides significantly better accuracy than alternative techniques for a wide range of computational budgets.

Connected through hyperedge HE-1 (induced by source paper "listen to look action recognition by previewing audio"):
  with members:
    - "what makes training multi modal networks hard"
    - "learning to localize sound sources in visual scenes analysis and applications"
    - "multi agent reinforcement learning based frame sampling for effective untrimmed video recognition"
    - "epic fusion audio visual temporal binding for egocentric action recognition"
    - "distinit learning video representations without a single labeled video"
    - "co separating sounds of visual objects"
    - "videobert a joint model for video and language representation learning"
    - "temporal cycle consistency learning"

Connected through hyperedge HE-2 (induced by source paper "adaframe adaptive frame selection for fast video recognition"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "autofocus efficient multi scale inference"
    - "recurrent residual module for fast inference in videos"
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "skipnet learning dynamic routing in convolutional networks"
    - "non local neural networks"
    - "compressed video action recognition"

Connected through hyperedge HE-3 (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"
    - "look ahead before you leap end to end active recognition by forecasting the effect of motion"
    - "pano2vid automatic cinematography for watching 360 circ videos"
    - "photo aesthetics ranking network with attributes and content adaptation"

Connected through hyperedge HE-4 (induced by source paper "ffnet video fast forwarding via reinforcement learning"):
  with members:
    - "egosampling wide view hyperlapse from egocentric videos"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "collaborative summarization of topic related videos"
    - "towards semantic fast forward and stabilized egocentric videos"
    - "poseagent budget constrained 6d object pose estimation via reinforcement learning"
    - "learning visual storylines with skipping recurrent neural networks"
    - "summary transfer exemplar based subset selection for video summarization"
    - "video summarization with long short term memory"

Connected through hyperedge HE-5 (induced by source paper "action representations in robotics a taxonomy and systematic classification"):
  with members:
    - "seamless integration and coordination of cognitive skills in humanoid robots a deep learning approach"
    - "semantic decomposition and recognition of long and complex manipulation action sequences"
    - "do what i want not what i did imitation of skills by planning sequences of actions"
    - "tensor representations via kernel linearization for action recognition from 3d skeletons"
    - "hand object interaction and precise localization in transitive action recognition"
    - "imagenet large scale visual recognition challenge"
    - "unstructured human activity detection from rgbd images"
    - "the symbol grounding problem"

Connected through hyperedge HE-6 (induced by source paper "skip rnn learning to skip state updates in recurrent neural networks"):
  with members:
    - "simple recurrent units for highly parallelizable recurrence"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"
    - "training a subsampling mechanism in expectation"
    - "a way out of the odyssey analyzing and combining recent insights for lstms"
    - "encouraging lstms to anticipate actions very early"
    - "learning to skim text"

Connected through hyperedge HE-7 (induced by source paper "blockdrop dynamic inference paths in residual networks"):
  with members:
    - "nisp pruning networks using neuron importance score propagation"
    - "data driven sparse structure selection for deep neural networks"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"
    - "training quantized nets a deeper understanding"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "multi scale dense networks for resource efficient image classification"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-8 (induced by source paper "next active object prediction from egocentric videos"):
  with members:
    - "first person action object detection with egonet"
    - "detecting engagement in egocentric video"
    - "simple online and realtime tracking"
    - "going deeper into first person activity recognition"
    - "compact cnn for indexing egocentric videos"
    - "you only look once unified real time object detection"
    - "predicting important objects for egocentric video summarization"
    - "anticipating visual representations from unlabeled video"

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)