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: "long term feature banks for detailed video understanding"
  Abstract: To understand the world, we humans constantly need to relate the present to the past, and put events in context. In this paper, we enable existing video models to do the same. We propose a long-term feature bank---supportive information extracted over the entire span of a video---to augment state-of-the-art video models that otherwise would only view short clips of 2-5 seconds. Our experiments demonstrate that augmenting 3D convolutional networks with a long-term feature bank yields state-of-the-art results on three challenging video datasets: AVA, EPIC-Kitchens, and Charades.

Connected through hyperedge HE-1 (induced by source paper "mast a memory augmented self supervised tracker"):
  with members:
    - "video object segmentation using space time memory networks"
    - "self supervised learning for video correspondence flow"
    - "multigrid predictive filter flow for unsupervised learning on videos"
    - "learning correspondence from the cycle consistency of time"
    - "joint task self supervised learning for temporal correspondence"
    - "unsupervised deep tracking"
    - "rvos end to end recurrent network for video object segmentation"
    - "feelvos fast end to end embedding learning for video object segmentation"

Connected through hyperedge HE-2 (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"
    - "binge watching scaling affordance learning from sitcoms"
    - "charades ego a large scale dataset of paired third and first person videos"

Connected through hyperedge HE-3 (induced by source paper "symbiotic attention with privileged information for egocentric action recognition"):
  with members:
    - "epic fusion audio visual temporal binding for egocentric action recognition"
    - "baidu uts submission to the epic kitchens action recognition challenge 2019"
    - "an evaluation of action recognition models on epic kitchens"
    - "large scale weakly supervised pre training for video action recognition"
    - "scaling egocentric vision the epic kitchens dataset"
    - "actor centric relation network"
    - "pairwise body part attention for recognizing human object interactions"
    - "learning human object interactions by graph parsing neural networks"

Connected through hyperedge HE-4 (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"
    - "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"
    - "zero shot anticipation for instructional activities"

Connected through hyperedge HE-5 (induced by source paper "hierarchical conditional relation networks for video question answering"):
  with members:
    - "learning to reason with relational video representation for question answering"
    - "progressive attention memory network for movie story question answering"
    - "heterogeneous memory enhanced multimodal attention model for video question answering"
    - "bilinear attention networks"
    - "non local netvlad encoding for video classification"
    - "hierarchical video frame sequence representation with deep convolutional graph network"
    - "motion appearance co memory networks for video question answering"
    - "multimodal dual attention memory for video story question answering"

Connected through hyperedge HE-6 (induced by source paper "accurate temporal action proposal generation with relation aware pyramid network"):
  with members:
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "multi granularity generator for temporal action proposal"
    - "ctap complementary temporal action proposal generation"
    - "reconstruction network for video captioning"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "soft nms improving object detection with one line of code"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"

Connected through hyperedge HE-7 (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-8 (induced by source paper "action genome actions as composition of spatio temporal scene graphs"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a short note on the kinetics 700 human action dataset"
    - "graphical contrastive losses for scene graph parsing"
    - "protogan towards few shot learning for action recognition"
    - "tarn temporal attentive relation network for few shot and zero shot action recognition"
    - "visual relationships as functions enabling few shot scene graph prediction"
    - "specifying object attributes and relations in interactive scene generation"
    - "scene graph prediction with limited labels"

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)