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: "mac mining activity concepts for language based temporal localization"
  Abstract: We address the problem of language-based temporal localization in untrimmed videos. Compared to temporal localization with fixed categories, this problem is more challenging as the language-based queries not only have no pre-defined activity list but also may contain complex descriptions. Previous methods address the problem by considering features from video sliding windows and language queries and learning a subspace to encode their correlation, which ignore rich semantic cues about activities in videos and queries. We propose to mine activity concepts from both video and language modalities by applying the actionness score enhanced Activity Concepts based Localizer (ACL). Specifically, the novel ACL encodes the semantic concepts from verb-obj pairs in language queries and leverages activity classifiers' prediction scores to encode visual concepts. Besides, ACL also has the capability to regress sliding windows as localization results. Experiments show that ACL significantly outperforms state-of-the-arts under the widely used metric, with more than 5% increase on both Charades-STA and TACoS datasets.

Connected through hyperedge HE-1 (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"
    - "tripping through time efficient localization of activities in videos"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "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-2 (induced by source paper "proposal free temporal moment localization of a natural language query in video using guided attention"):
  with members:
    - "tripping through time efficient localization of activities in videos"
    - "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"
    - "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-3 (induced by source paper "semantic conditioned dynamic modulation for temporal sentence grounding in videos"):
  with members:
    - "sentence specified dynamic video thumbnail generation"
    - "spatio temporal video re localization by warp lstm"
    - "weakly supervised spatio temporally grounding natural sentence in video"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "video re localization"
    - "multi granularity generator for temporal action proposal"
    - "multilevel language and vision integration for text to clip retrieval"
    - "actor and action video segmentation from a sentence"

Connected through hyperedge HE-4 (induced by source paper "temporally grounding language queries in videos by contextual boundary aware prediction"):
  with members:
    - "tripping through time efficient localization of activities in videos"
    - "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-5 (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"
    - "tripping through time efficient localization of activities in videos"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "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-6 (induced by source paper "exploiting temporal relationships in video moment localization with natural language"):
  with members:
    - "improving referring expression grounding with cross modal attention guided erasing"
    - "read watch and move reinforcement learning for temporally grounding natural language descriptions in videos"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "mattnet modular attention network for referring expression comprehension"
    - "multilevel language and vision integration for text to clip retrieval"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "constituency parsing with a self attentive encoder"
    - "grounding referring expressions in images by variational context"

Connected through hyperedge HE-7 (induced by source paper "compositional temporal visual grounding of natural language event descriptions"):
  with members:
    - "wman weakly supervised moment alignment network for text based video segment retrieval"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "w talc weakly supervised temporal activity localization and classification"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "weakly supervised action localization by sparse temporal pooling network"
    - "tall temporal activity localization via language query"
    - "localizing moments in video with natural language"
    - "connectionist temporal modeling for weakly supervised action labeling"

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)