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: "acd action concept discovery from image sentence corpora"
  Abstract: Action classification in still images is an important task in computer vision. It is challenging as the appearances of ac- tions may vary depending on their context (e.g. associated objects). Manually labeling of context information would be time consuming and difficult to scale up. To address this challenge, we propose a method to automatically discover and cluster action concepts, and learn their classifiers from weakly supervised image-sentence corpora. It obtains candidate action concepts by extracting verb-object pairs from sentences and verifies their visualness with the associated images. Candidate action concepts are then clustered by using a multi-modal representation with image embeddings from deep convolutional networks and text embeddings from word2vec. More than one hundred human action concept classifiers are learned from the Flickr 30k dataset with no additional human effort and promising classification results are obtained. We further apply the AdaBoost algorithm to automatically select and combine relevant action concepts given an action query. Promising results have been shown on the PASCAL VOC 2012 action classification benchmark, which has zero overlap with Flickr30k.

Connected through hyperedge HE-1 (induced by source paper "read watch and move reinforcement learning for temporally grounding natural language descriptions in videos"):
  with members:
    - "single shot temporal action detection"
    - "video captioning via hierarchical reinforcement learning"
    - "temporal action detection with structured segment networks"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "dense captioning events in videos"
    - "weakly supervised visual grounding of phrases with linguistic structures"
    - "tall temporal activity localization via language query"
    - "localizing moments in video with natural language"

Connected through hyperedge HE-2 (induced by source paper "mac mining activity concepts for language based temporal localization"):
  with members:
    - "knowledge aided consistency for weakly supervised phrase grounding"
    - "ctap complementary temporal action proposal generation"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "query guided regression network with context policy for phrase grounding"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "temporal action detection with structured segment networks"
    - "cascaded boundary regression for temporal action detection"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"

Connected through hyperedge HE-3 (induced by source paper "tall temporal activity localization via language query"):
  with members:
    - "turn tap temporal unit regression network for temporal action proposals"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "generation and comprehension of unambiguous object descriptions"
    - "end to end learning of action detection from frame glimpses in videos"
    - "you only look once unified real time object detection"
    - "skip thought vectors"
    - "beyond short snippets deep networks for video classification"

Connected through hyperedge HE-4 (induced by source paper "learning action concept trees and semantic alignment networks from image description data"):
  with members:
    - "segmentation from natural language expressions"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "contextual action recognition with r cnn"
    - "a dataset for movie description"
    - "automatic concept discovery from parallel text and visual corpora"
    - "unsupervised learning from narrated instruction videos"
    - "using descriptive video services to create a large data source for video annotation research"
    - "natural language object retrieval"

Connected through hyperedge HE-5 (induced by source paper "action classification via concepts and attributes"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "learning models for actions and person object interactions with transfer to question answering"
    - "weakly supervised deep detection networks"
    - "deep spatial pyramid the devil is once again in the details"
    - "deep residual learning for image recognition"
    - "constrained convolutional neural networks for weakly supervised segmentation"

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)