human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "proposal free temporal moment localization of a natural language query in video using guided attention"):
  Abstract: This paper studies the problem of temporal moment localization in a long untrimmed video using natural language as the query. Given an untrimmed video and a sentence as the query, the goal is to determine the starting, and the ending, of the relevant visual moment in the video, that corresponds to the query sentence. While previous works have tackled this task by a propose-and-rank approach, we introduce a more efficient, end-to-end trainable, and {\em proposal-free approach} that relies on three key components: a dynamic filter to transfer language information to the visual domain, a new loss function to guide our model to attend the most relevant parts of the video, and soft labels to model annotation uncertainty. We evaluate our method on two benchmark datasets, Charades-STA and ActivityNet-Captions. Experimental results show that our approach outperforms state-of-the-art methods on both datasets.

Includes members:
  Member "tripping through time efficient localization of activities in videos":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "weakly supervised multi level attentional reconstruction network for grounding textual queries in videos")
  Member "excl extractive clip localization using natural language descriptions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tvr a large scale dataset for video subtitle moment retrieval")
  Member "man moment alignment network for natural language moment retrieval via iterative graph adjustment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "where does it exist spatio temporal video grounding for multi form sentences")
  Member "diagnosing error in temporal action detectors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "refineloc iterative refinement for weakly supervised action localization")
  Member "mac mining activity concepts for language based temporal localization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tree structured policy based progressive reinforcement learning for temporally language grounding in video")
  Member "multilevel language and vision integration for text to clip retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "where does it exist spatio temporal video grounding for multi form sentences")
  Member "neuralnetwork viterbi a framework for weakly supervised video learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks")
  Member "to find where you talk temporal sentence localization in video with attention based location regression":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "weakly supervised multi level attentional reconstruction network for grounding textual queries in videos")

Question: Please tell me which class the source paper 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)