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: "on the effectiveness of task granularity for transfer learning"
  Abstract: We describe a DNN for video classification and captioning, trained end-to-end, with shared features, to solve tasks at different levels of granularity, exploring the link between granularity in a source task and the quality of learned features for transfer learning. For solving the new task domain in transfer learning, we freeze the trained encoder and fine-tune a neural net on the target domain. We train on the Something-Something dataset with over 220, 000 videos, and multiple levels of target granularity, including 50 action groups, 174 fine-grained action categories and captions. Classification and captioning with Something-Something are challenging because of the subtle differences between actions, applied to thousands of different object classes, and the diversity of captions penned by crowd actors. Our model performs better than existing classification baselines for SomethingSomething, with impressive fine-grained results. And it yields a strong baseline on the new Something-Something captioning task. Experiments reveal that training with more fine-grained tasks tends to produce better features for transfer learning.

Connected through hyperedge HE-1 (induced by source paper "understanding contexts inside robot and human manipulation tasks through a vision language model and ontology system in a video stream"):
  with members:
    - "manipulation skill assessment from videos with spatial attention network"
    - "combined task and action learning from human demonstrations for mobile manipulation applications"
    - "v2cnet a deep learning framework to translate videos to commands for robotic manipulation"
    - "robot learning and execution of collaborative manipulation plans from youtube videos"
    - "integrating knowledge and reasoning in image understanding"
    - "improving grounded natural language understanding through human robot dialog"
    - "learning actions from human demonstration video for robotic manipulation"
    - "prospection interpretable plans from language by predicting the future"

Connected through hyperedge HE-2 (induced by source paper "temporal reasoning graph for activity recognition"):
  with members:
    - "visual query answering by entity attribute graph matching and reasoning"
    - "linkage based face clustering via graph convolution network"
    - "multi label image recognition with graph convolutional networks"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "eco efficient convolutional network for online video understanding"
    - "graph neural networks a review of methods and applications"
    - "learning discriminative video representations using adversarial perturbations"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"

Connected through hyperedge HE-3 (induced by source paper "paying more attention to motion attention distillation for learning video representations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deeply supervised salient object detection with short connections"
    - "end to end learning of motion representation for video understanding"
    - "what have we learned from deep representations for action recognition"
    - "end to end multi task learning with attention"
    - "modality distillation with multiple stream networks for action recognition"
    - "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 "reasoning about human object interactions through dual attention networks"):
  with members:
    - "learning human object interactions by graph parsing neural networks"
    - "videos as space time region graphs"
    - "tsm temporal shift module for efficient video understanding"
    - "detecting and recognizing human object interactions"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "squeeze and excitation networks"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-5 (induced by source paper "grouped spatial temporal aggregation for efficient action recognition"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "long term feature banks for detailed video understanding"
    - "end to end learning of motion representation for video understanding"
    - "scaling egocentric vision the epic kitchens dataset"
    - "motion feature network fixed motion filter for action recognition"
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "videos as space time region graphs"
    - "slowfast networks for video recognition"

Connected through hyperedge HE-6 (induced by source paper "cooperative cross stream network for discriminative action representation"):
  with members:
    - "learning discriminative video representations using adversarial perturbations"
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "video representation learning using discriminative pooling"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "in defense of the triplet loss for person re identification"
    - "non local neural networks"

Connected through hyperedge HE-7 (induced by source paper "fine grained action retrieval through multiple parts of speech embeddings"):
  with members:
    - "action2vec a crossmodal embedding approach to action learning"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "higher order network for action recognition"
    - "scaling egocentric vision the epic kitchens dataset"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "a joint sequence fusion model for video question answering and retrieval"
    - "cross modal embeddings for video and audio retrieval"
    - "predicting visual features from text for image and video caption retrieval"

Connected through hyperedge HE-8 (induced by source paper "repair removing representation bias by dataset resampling"):
  with members:
    - "women also snowboard overcoming bias in captioning models"
    - "what have we learned from deep representations for action recognition"
    - "recognition in terra incognita"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "convnets and imagenet beyond accuracy understanding mistakes and uncovering biases"
    - "the kinetics human action video dataset"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"

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)