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: "weakly supervised video moment retrieval from text queries"
  Abstract: There have been a few recent methods proposed in text to video moment retrieval using natural language queries, but requiring full supervision during training. However, acquiring a large number of training videos with temporal boundary annotations for each text description is extremely time-consuming and often not scalable. In order to cope with this issue, in this work, we introduce the problem of learning from weak labels for the task of text to video moment retrieval. The weak nature of the supervision is because, during training, we only have access to the video-text pairs rather than the temporal extent of the video to which different text descriptions relate. We propose a joint visual-semantic embedding based framework that learns the notion of relevant segments from video using only video-level sentence descriptions. Specifically, our main idea is to utilize latent alignment between video frames and sentence descriptions using Text-Guided Attention (TGA). TGA is then used during the test phase to retrieve relevant moments. Experiments on two benchmark datasets demonstrate that our method achieves comparable performance to state-of-the-art fully supervised approaches.

Connected through hyperedge HE-1 (induced by source paper "where does it exist spatio temporal video grounding for multi form sentences"):
  with members:
    - "dynamic graph attention for referring expression comprehension"
    - "cross modal interaction networks for query based moment retrieval in videos"
    - "relation aware graph attention network for visual question answering"
    - "cross modal relationship inference for grounding referring expressions"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "object referring in videos with language and human gaze"
    - "mattnet modular attention network for referring expression comprehension"
    - "multilevel language and vision integration for text to clip retrieval"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised multi level attentional reconstruction network for grounding textual queries in videos"):
  with members:
    - "look closer to ground better weakly supervised temporal grounding of sentence in video"
    - "semantic conditioned dynamic modulation for temporal sentence grounding in videos"
    - "tripping through time efficient localization of activities in videos"
    - "temporally grounding language queries in videos by contextual boundary aware prediction"
    - "bmn boundary matching network for temporal action proposal generation"
    - "wman weakly supervised moment alignment network for text based video segment retrieval"
    - "multilevel language and vision integration for text to clip retrieval"
    - "to find where you talk temporal sentence localization in video with attention based location regression"

Connected through hyperedge HE-3 (induced by source paper "look closer to ground better weakly supervised temporal grounding of sentence in video"):
  with members:
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "weakly supervised dense event captioning in videos"
    - "mattnet modular attention network for referring expression comprehension"
    - "multilevel language and vision integration for text to clip retrieval"
    - "weakly supervised video object grounding from text by loss weighting and object interaction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "to find where you talk temporal sentence localization in video with attention based location regression"
    - "grounding referring expressions in images by variational context"

Connected through hyperedge HE-4 (induced by source paper "action modifiers learning from adverbs in instructional videos"):
  with members:
    - "manipulation skill assessment from videos with spatial attention network"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "wman weakly supervised moment alignment network for text based video segment retrieval"
    - "human action adverb recognition adha dataset and a three stream hybrid model"
    - "adversarial soft detection based aggregation network for image retrieval"
    - "motion appearance co memory networks for video question answering"
    - "attributes as operators factorizing unseen attribute object compositions"
    - "w talc weakly supervised temporal activity localization and classification"

Connected through hyperedge HE-5 (induced by source paper "wman weakly supervised moment alignment network for text based video segment retrieval"):
  with members:
    - "language conditioned graph networks for relational reasoning"
    - "excl extractive clip localization using natural language descriptions"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "stacked cross attention for image text matching"
    - "multilevel language and vision integration for text to clip retrieval"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "to find where you talk temporal sentence localization in video with attention based location regression"
    - "query guided regression network with context policy for phrase grounding"

Connected through hyperedge HE-6 (induced by source paper "weakly supervised video moment retrieval via semantic completion network"):
  with members:
    - "cross modal interaction networks for query based moment retrieval in videos"
    - "mass masked sequence to sequence pre training for language generation"
    - "language models with transformers"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "weakly supervised dense event captioning in videos"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "multilevel language and vision integration for text to clip retrieval"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)