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: "tripping through time efficient localization of activities in videos"
  Abstract: Localizing moments in untrimmed videos via language queries is a new and interesting task that requires the ability to accurately ground language into video. Previous works have approached this task by processing the entire video, often more than once, to localize relevant activities. In the real world applications that this task lends itself to, such as surveillance, efficiency is a pivotal trait of a system. In this paper, we present TripNet, an end-to-end system that uses a gated attention architecture to model fine-grained textual and visual representations in order to align text and video content. Furthermore, TripNet uses reinforcement learning to efficiently localize relevant activity clips in long videos, by learning how to intelligently skip around the video. It extracts visual features for fewer frames to perform activity classification. In our evaluation over Charades-STA, ActivityNet Captions and the TACoS dataset, we find that TripNet achieves high accuracy and saves processing time by only looking at 32-41% of the entire video.

Connected through hyperedge HE-1 (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"
    - "weakly supervised video moment retrieval from text queries"
    - "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-2 (induced by source paper "tree structured policy based progressive reinforcement learning for temporally language grounding in video"):
  with members:
    - "read watch and move reinforcement learning for temporally grounding natural language descriptions in videos"
    - "multi agent reinforcement learning based frame sampling for effective untrimmed video recognition"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "mac mining activity concepts for language based temporal localization"
    - "to find where you talk temporal sentence localization in video with attention based location regression"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "dense captioning events in videos"

Connected through hyperedge HE-3 (induced by source paper "proposal free temporal moment localization of a natural language query in video using guided attention"):
  with members:
    - "excl extractive clip localization using natural language descriptions"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "diagnosing error in temporal action detectors"
    - "mac mining activity concepts for language based temporal localization"
    - "multilevel language and vision integration for text to clip retrieval"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "to find where you talk temporal sentence localization in video with attention based location regression"
    - "rethinking the faster r cnn architecture for temporal action localization"

Connected through hyperedge HE-4 (induced by source paper "prolonets neural encoding human experts domain knowledge to warm start reinforcement learning"):
  with members:
    - "imitating latent policies from observation"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "adaptive neural trees"
    - "fast policy learning through imitation and reinforcement"
    - "deep reinforcement learning that matters"

Connected through hyperedge HE-5 (induced by source paper "temporally grounding language queries in videos by contextual boundary aware prediction"):
  with members:
    - "mac mining activity concepts for language based temporal localization"
    - "mattnet modular attention network for referring expression comprehension"
    - "multilevel language and vision integration for text to clip retrieval"
    - "query guided regression network with context policy for phrase grounding"
    - "grounding referring expressions in images by variational context"
    - "attention is all you need"
    - "dense captioning events in videos"
    - "tall temporal activity localization via language query"

Connected through hyperedge HE-6 (induced by source paper "learning 2d temporal adjacent networks for moment localization with natural language"):
  with members:
    - "exploiting temporal relationships in video moment localization with natural language"
    - "cross modal interaction networks for query based moment retrieval in videos"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "mac mining activity concepts for language based temporal localization"
    - "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"
    - "tvqa localized compositional video question answering"
    - "single shot temporal action detection"

Connected through hyperedge HE-7 (induced by source paper "neural encoding human experts domain knowledge to warm start reinforcement learning"):
  with members:
    - "imitating latent policies from observation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "fast policy learning through imitation and reinforcement"
    - "lifelong learning with dynamically expandable networks"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "proximal policy optimization algorithms"

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)