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: "actionvlad learning spatio temporal aggregation for action classification"
  Abstract: In this work, we introduce a new video representation for action classification that aggregates local convolutional features across the entire spatio-temporal extent of the video. We do so by integrating state-of-the-art two-stream networks with learnable spatio-temporal feature aggregation. The resulting architecture is end-to-end trainable for whole-video classification. We investigate different strategies for pooling across space and time and combining signals from the different streams. We find that: (i) it is important to pool jointly across space and time, but (ii) appearance and motion streams are best aggregated into their own separate representations. Finally, we show that our representation outperforms the two-stream base architecture by a large margin (13% relative) as well as out-performs other baselines with comparable base architectures on HMDB51, UCF101, and Charades video classification benchmarks.

Connected through hyperedge HE-1 (induced by source paper "ego topo environment affordances from egocentric video"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "videograph recognizing minutes long human activities in videos"
    - "anticipation and next action forecasting in video an end to end model with memory"
    - "generative hybrid representations for activity forecasting with no regret learning"
    - "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"
    - "long term feature banks for detailed video understanding"
    - "binge watching scaling affordance learning from sitcoms"

Connected through hyperedge HE-2 (induced by source paper "pic permutation invariant convolution for recognizing long range activities"):
  with members:
    - "videograph recognizing minutes long human activities in videos"
    - "large memory layers with product keys"
    - "long term feature banks for detailed video understanding"
    - "interaction aware spatio temporal pyramid attention networks for action classification"
    - "scaling egocentric vision the epic kitchens dataset"
    - "video time properties encoders and evaluation"
    - "video action transformer network"
    - "timeception for complex action recognition"

Connected through hyperedge HE-3 (induced by source paper "a hybrid rnn hmm approach for weakly supervised temporal action segmentation"):
  with members:
    - "weakly supervised action segmentation with iterative soft boundary assignment"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "weakly supervised action localization by sparse temporal pooling network"
    - "the kinetics human action video dataset"
    - "connectionist temporal modeling for weakly supervised action labeling"
    - "state duration and interval modeling in hidden semi markov model for sequential data analysis"

Connected through hyperedge HE-4 (induced by source paper "dynamic inference a new approach toward efficient video action recognition"):
  with members:
    - "multi agent reinforcement learning based frame sampling for effective untrimmed video recognition"
    - "eco efficient convolutional network for online video understanding"
    - "multi fiber networks for video recognition"
    - "stnet local and global spatial temporal modeling for action recognition"
    - "motion feature network fixed motion filter for action recognition"
    - "tsm temporal shift module for efficient video understanding"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"

Connected through hyperedge HE-5 (induced by source paper "sensor fusion gated recurrent fusion to learn driving behavior from temporal multimodal data"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "online action detection in untrimmed streaming videos modeling and evaluation"
    - "learning to drive in a day"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "lstm a search space odyssey"

Connected through hyperedge HE-6 (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-7 (induced by source paper "dynamonet dynamic action and motion network"):
  with members:
    - "visual dynamics stochastic future generation via layered cross convolutional networks"
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "self supervised learning of face representations for video face clustering"
    - "learning human optical flow"
    - "spatio temporal channel correlation networks for action classification"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "d3d distilled 3d networks for video action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-8 (induced by source paper "cater a diagnostic dataset for compositional actions and temporal reasoning"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "learning visual question answering by bootstrapping hard attention"
    - "distractor aware siamese networks for visual object tracking"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "revisiting the effectiveness of off the shelf temporal modeling approaches for large scale video classification"
    - "non local neural networks"

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)