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: "adaframe adaptive frame selection for fast video recognition"
  Abstract: We present AdaFrame, a framework that adaptively selects relevant frames on a per-input basis for fast video recognition. AdaFrame contains a Long Short-Term Memory network augmented with a global memory that provides context information for searching which frames to use over time. Trained with policy gradient methods, AdaFrame generates a prediction, determines which frame to observe next, and computes the utility, i.e., expected future rewards, of seeing more frames at each time step. At testing time, AdaFrame exploits predicted utilities to achieve adaptive lookahead inference such that the overall computational costs are reduced without incurring a decrease in accuracy. Extensive experiments are conducted on two large-scale video benchmarks, FCVID and ActivityNet. AdaFrame matches the performance of using all frames with only 8.21 and 8.65 frames on FCVID and ActivityNet, respectively. We further qualitatively demonstrate learned frame usage can indicate the difficulty of making classification decisions; easier samples need fewer frames while harder ones require more, both at instance-level within the same class and at class-level among different categories.

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 "scsampler sampling salient clips from video for efficient action recognition"):
  with members:
    - "video classification with channel separated convolutional networks"
    - "long term feature banks for detailed video understanding"
    - "learning discriminative video representations using adversarial perturbations"
    - "learning to separate object sounds by watching unlabeled video"
    - "slowfast networks for video recognition"
    - "the sound of pixels"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "audio visual scene analysis with self supervised multisensory features"

Connected through hyperedge HE-3 (induced by source paper "faster recurrent networks for efficient video classification"):
  with members:
    - "efficient video classification using fewer frames"
    - "scsampler sampling salient clips from video for efficient action recognition"
    - "eco efficient convolutional network for online video understanding"
    - "multi fiber networks for video recognition"
    - "videos as space time region graphs"
    - "spatio temporal channel correlation networks for action classification"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-4 (induced by source paper "multi agent reinforcement learning based frame sampling for effective untrimmed video recognition"):
  with members:
    - "read watch and move reinforcement learning for temporally grounding natural language descriptions in videos"
    - "eco efficient convolutional network for online video understanding"
    - "multi fiber networks for video recognition"
    - "stnet local and global spatial temporal modeling for action recognition"
    - "fine grained video categorization with redundancy reduction attention"
    - "motion feature network fixed motion filter for action recognition"
    - "compact generalized non local network"
    - "videos as space time region graphs"

Connected through hyperedge HE-5 (induced by source paper "liteeval a coarse to fine framework for resource efficient video recognition"):
  with members:
    - "scsampler sampling salient clips from video for efficient action recognition"
    - "eco efficient convolutional network for online video understanding"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "autofocus efficient multi scale inference"
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "sniper efficient multi scale training"
    - "slowfast networks for video recognition"
    - "skipnet learning dynamic routing in convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "startnet online detection of action start in untrimmed videos"):
  with members:
    - "online action detection in untrimmed streaming videos modeling and evaluation"
    - "temporal recurrent networks for online action detection"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "temporal action detection with structured segment networks"
    - "blockdrop dynamic inference paths in residual networks"
    - "temporal context network for activity localization in videos"

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)