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: "selecting relevant web trained concepts for automated event retrieval"
  Abstract: Complex event retrieval is a challenging research problem, especially when no training videos are available. An alternative to collecting training videos is to train a large semantic concept bank a priori. Given a text description of an event, event retrieval is performed by selecting concepts linguistically related to the event description and fusing the concept responses on unseen videos. However, defining an exhaustive concept lexicon and pre-training it requires vast computational resources. Therefore, recent approaches automate concept discovery and training by leveraging large amounts of weakly annotated web data. Compact visually salient concepts are automatically obtained by the use of concept pairs or, more generally, n-grams. However, not all visually salient n-grams are necessarily useful for an event query--some combinations of concepts may be visually compact but irrelevant--and this drastically affects performance. We propose an event retrieval algorithm that constructs pairs of automatically discovered concepts and then prunes those concepts that are unlikely to be helpful for retrieval. Pruning depends both on the query and on the specific video instance being evaluated. Our approach also addresses calibration and domain adaptation issues that arise when applying concept detectors to unseen videos. We demonstrate large improvements over other vision based systems on the TRECVID MED 13 dataset.

Connected through hyperedge HE-1 (induced by source paper "image to video domain adaptation using web supervision"):
  with members:
    - "importance weighted adversarial nets for partial domain adaptation"
    - "slowfast networks for video recognition"
    - "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"
    - "non local neural networks"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "attention transfer from web images for video recognition"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "domain separation networks"
    - "webly supervised learning of convolutional networks"
    - "simultaneous deep transfer across domains and tasks"
    - "unsupervised learning of visual representations using videos"
    - "learning transferable features with deep adaptation networks"
    - "beyond short snippets deep networks for video classification"

Connected through hyperedge HE-3 (induced by source paper "learning without prejudice avoiding bias in webly supervised action recognition"):
  with members:
    - "the thumos challenge on action recognition for videos in the wild"
    - "temporal segment networks towards good practices for deep action recognition"
    - "sympathy for the details dense trajectories and hybrid classification architectures for action recognition"
    - "yfcc100m the new data in multimedia research"
    - "socializing the semantic gap a comparative survey on image tag assignment refinement and retrieval"
    - "webly supervised learning of convolutional networks"
    - "a data driven approach for tag refinement and localization in web videos"
    - "unsupervised learning of video representations using lstms"

Connected through hyperedge HE-4 (induced by source paper "automatic spatially aware fashion concept discovery"):
  with members:
    - "learning fashion compatibility with bidirectional lstms"
    - "automatic attribute discovery with neural activations"
    - "disentangling nonlinear perceptual embeddings with multi query triplet networks"
    - "end to end localization and ranking for relative attributes"
    - "learning deep structure preserving image text embeddings"
    - "cross domain image retrieval with a dual attribute aware ranking network"
    - "automatic concept discovery from parallel text and visual corpora"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-5 (induced by source paper "twitter100k a real world dataset for weakly supervised cross media retrieval"):
  with members:
    - "person re identification past present and future"
    - "compound rank k projections for bilinear analysis"
    - "sift meets cnn a decade survey of instance retrieval"
    - "learning deep structure preserving image text embeddings"
    - "zero shot event detection by multimodal distributional semantic embedding of videos"
    - "multimodal convolutional neural networks for matching image and sentence"
    - "very deep convolutional networks for large scale image recognition"
    - "packing and padding coupled multi index for accurate image retrieval"

Connected through hyperedge HE-6 (induced by source paper "complex event recognition from images with few training examples"):
  with members:
    - "webly supervised learning of convolutional networks"
    - "eventnet a large scale structured concept library for complex event detection in video"
    - "automatic concept discovery from parallel text and visual corpora"
    - "deep learning for single view instance recognition"
    - "building a large concept bank for representing events in video"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "conceptlearner discovering visual concepts from weakly labeled image collections"

Connected through hyperedge HE-7 (induced by source paper "self reinforced cascaded regression for face alignment"):
  with members:
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "learning from simulated and unsupervised images through adversarial training"
    - "robust face alignment using a mixture of invariant experts"

Connected through hyperedge HE-8 (induced by source paper "video stream retrieval of unseen queries using semantic memory"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "the imagenet shuffle reorganized pre training for video event detection"
    - "yfcc100m the new data in multimedia research"
    - "objects2action classifying and localizing actions without any video example"
    - "going deeper with convolutions"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "imagenet large scale visual recognition challenge"
    - "a discriminative cnn video representation for event 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)