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: "multilevel language and vision integration for text to clip retrieval"
  Abstract: We address the problem of text-based activity retrieval in video. Given a sentence describing an activity, our task is to retrieve matching clips from an untrimmed video. To capture the inherent structures present in both text and video, we introduce a multilevel model that integrates vision and language features earlier and more tightly than prior work. First, we inject text features early on when generating clip proposals, to help eliminate unlikely clips and thus speed up processing and boost performance. Second, to learn a fine-grained similarity metric for retrieval, we use visual features to modulate the processing of query sentences at the word level in a recurrent neural network. A multi-task loss is also employed by adding query re-generation as an auxiliary task. Our approach significantly outperforms prior work on two challenging benchmarks: Charades-STA and ActivityNet Captions.

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"
    - "weakly supervised video moment retrieval from text queries"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "tvr a large scale dataset for video subtitle moment retrieval"):
  with members:
    - "tvqa spatio temporal grounding for video question answering"
    - "vatex a large scale high quality multilingual dataset for video and language research"
    - "temporal localization of moments in video collections with natural language"
    - "roberta a robustly optimized bert pretraining approach"
    - "excl extractive clip localization using natural language descriptions"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "mattnet modular attention network for referring expression comprehension"

Connected through hyperedge HE-3 (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"
    - "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"
    - "to find where you talk temporal sentence localization in video with attention based location regression"

Connected through hyperedge HE-4 (induced by source paper "look closer to ground better weakly supervised temporal grounding of sentence in video"):
  with members:
    - "weakly supervised video moment retrieval from text queries"
    - "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"
    - "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-5 (induced by source paper "adversarial representation learning for text to image matching"):
  with members:
    - "d sne domain adaptation using stochastic neighborhood embedding"
    - "mirrorgan learning text to image generation by redescription"
    - "in defense of the triplet loss for visual recognition"
    - "boosting standard classification architectures through a ranking regularizer"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "partial adversarial domain adaptation"
    - "deep contextualized word representations"
    - "learnable pins cross modal embeddings for person identity"

Connected through hyperedge HE-6 (induced by source paper "language features matter effective language representations for vision language tasks"):
  with members:
    - "bilinear attention networks"
    - "stacked cross attention for image text matching"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "deep contextualized word representations"
    - "open vocabulary phrase detection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "query guided regression network with context policy for phrase grounding"

Connected through hyperedge HE-7 (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"
    - "mac mining activity concepts for language based temporal localization"
    - "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-8 (induced by source paper "tripping through time efficient localization of activities in videos"):
  with members:
    - "read watch and move reinforcement learning for temporally grounding natural language descriptions in videos"
    - "learning to localize and align fine grained actions to sparse instructions"
    - "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"
    - "revisiting the effectiveness of off the shelf temporal modeling approaches for large scale video classification"
    - "attend and interact higher order object interactions for video understanding"
    - "gated attention architectures for task oriented language grounding"
    - "learnable pooling with context gating for video classification"

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)