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: "temporal localization of fine grained actions in videos by domain transfer from web images"
  Abstract: We address the problem of fine-grained action localization from temporally untrimmed web videos. We assume that only weak video-level annotations are available for training. The goal is to use these weak labels to identify temporal segments corresponding to the actions, and learn models that generalize to unconstrained web videos. We find that web images queried by action names serve as well-localized highlights for many actions, but are noisily labeled. To solve this problem, we propose a simple yet effective method that takes weak video labels and noisy image labels as input, and generates localized action frames as output. This is achieved by cross-domain transfer between video frames and web images, using pre-trained deep convolutional neural networks. We then use the localized action frames to train action recognition models with long short-term memory networks. We collect a fine-grained sports action data set FGA-240 of more than 130,000 YouTube videos. It has 240 fine-grained actions under 85 sports activities. Convincing results are shown on the FGA-240 data set, as well as the THUMOS 2014 localization data set with untrimmed training videos.

Connected through hyperedge HE-1 (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"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "the kinetics human action video dataset"
    - "connectionist temporal modeling for weakly supervised action labeling"

Connected through hyperedge HE-2 (induced by source paper "set constrained viterbi for set supervised action segmentation"):
  with members:
    - "weakly supervised energy based learning for action segmentation"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "w talc weakly supervised temporal activity localization and classification"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "weakly supervised action segmentation with iterative soft boundary assignment"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "action sets weakly supervised action segmentation without ordering constraints"
    - "untrimmednets for weakly supervised action recognition and detection"

Connected through hyperedge HE-3 (induced by source paper "lpat learning to predict adaptive threshold for weakly supervised temporal action localization"):
  with members:
    - "dmc net generating discriminative motion cues for fast compressed video action recognition"
    - "segregated temporal assembly recurrent networks for weakly supervised multiple action detection"
    - "cascaded pyramid mining network for weakly supervised temporal action localization"
    - "w talc weakly supervised temporal activity localization and classification"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised gaussian networks for action detection"):
  with members:
    - "w talc weakly supervised temporal activity localization and classification"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "weakly supervised action segmentation with iterative soft boundary assignment"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "action sets weakly supervised action segmentation without ordering constraints"
    - "what actions are needed for understanding human actions in videos"
    - "discriminatively learned hierarchical rank pooling networks"

Connected through hyperedge HE-5 (induced by source paper "videos as space time region graphs"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "segmentation aware convolutional networks using local attention masks"
    - "joint discovery of object states and manipulation actions"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "detecting and recognizing human object interactions"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "webly supervised joint embedding for cross modal image text retrieval"):
  with members:
    - "exploring stereotypes and biased data with the crowd"
    - "sampling matters in deep embedding learning"
    - "learning two branch neural networks for image text matching tasks"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "dual path convolutional image text embedding with instance loss"
    - "learning robust visual semantic embeddings"
    - "vse improving visual semantic embeddings with hard negatives"
    - "learning social image embedding with deep multimodal attention networks"

Connected through hyperedge HE-7 (induced by source paper "online detection of action start in untrimmed streaming videos"):
  with members:
    - "multi fiber networks for video recognition"
    - "ctap complementary temporal action proposal generation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "good semi supervised learning that requires a bad gan"
    - "single shot temporal action detection"
    - "predictive corrective networks for action detection"
    - "temporal action detection with structured segment networks"
    - "cascaded boundary regression for temporal action detection"

Connected through hyperedge HE-8 (induced by source paper "step by step erasion one by one collection a weakly supervised temporal action detector"):
  with members:
    - "monocular depth estimation using multi scale continuous crfs as sequential deep networks"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"
    - "temporal segment networks for action recognition in videos"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "untrimmednets for weakly supervised action recognition and detection"

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)