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 alignment of video with text"
  Abstract: Suppose that we are given a set of videos, along with natural language descriptions in the form of multiple sentences (e.g., manual annotations, movie scripts, sport summaries etc.), and that these sentences appear in the same temporal order as their visual counterparts. We propose in this paper a method for aligning the two modalities, i.e., automatically providing a time stamp for every sentence. Given vectorial features for both video and text, we propose to cast this task as a temporal assignment problem, with an implicit linear mapping between the two feature modalities. We formulate this problem as an integer quadratic program, and solve its continuous convex relaxation using an efficient conditional gradient algorithm. Several rounding procedures are proposed to construct the final integer solution. After demonstrating significant improvements over the state of the art on the related task of aligning video with symbolic labels [7], we evaluate our method on a challenging dataset of videos with associated textual descriptions [36], using both bag-of-words and continuous representations for text.

Connected through hyperedge HE-1 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks"):
  with members:
    - "marginalized average attentional network for weakly supervised learning"
    - "dynamic graph modules for modeling higher order interactions in activity recognition"
    - "segregated temporal assembly recurrent networks for weakly supervised multiple action detection"
    - "w talc weakly supervised temporal activity localization and classification"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "relational inductive biases deep learning and graph networks"
    - "dynamic graph modules for modeling object object interactions in activity recognition"
    - "a structured model for action detection"

Connected through hyperedge HE-2 (induced by source paper "a hybrid rnn hmm approach for weakly supervised temporal action segmentation"):
  with members:
    - "weakly supervised action segmentation with iterative soft boundary assignment"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "weakly supervised action localization by sparse temporal pooling network"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "the kinetics human action video dataset"
    - "connectionist temporal modeling for weakly supervised action labeling"

Connected through hyperedge HE-3 (induced by source paper "modularized textual grounding for counterfactual resilience"):
  with members:
    - "weakly supervised attention learning for textual phrases grounding"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "w talc weakly supervised temporal activity localization and classification"
    - "mattnet modular attention network for referring expression comprehension"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "generating counterfactual explanations with natural language"
    - "exploring the limits of weakly supervised pretraining"
    - "unsupervised textual grounding linking words to image concepts"

Connected through hyperedge HE-4 (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"
    - "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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "unsupervised discovery of multimodal links in multi image multi sentence documents"):
  with members:
    - "align2ground weakly supervised phrase grounding guided by image caption alignment"
    - "dynamic multimodal instance segmentation guided by natural language queries"
    - "recipeqa a challenge dataset for multimodal comprehension of cooking recipes"
    - "quantifying the visual concreteness of words and topics in multimodal datasets"
    - "exploring the limits of weakly supervised pretraining"
    - "learning transferable architectures for scalable image recognition"
    - "weakly supervised learning of visual relations"
    - "segmentation from natural language expressions"

Connected through hyperedge HE-6 (induced by source paper "revisiting image language networks for open ended phrase detection"):
  with members:
    - "lvis a dataset for large vocabulary instance segmentation"
    - "language features matter effective language representations for vision language tasks"
    - "neural sequential phrase grounding seqground"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "stacked cross attention for image text matching"
    - "discriminative bimodal networks for visual localization and detection with natural language queries"
    - "query guided regression network with context policy for phrase grounding"
    - "learning two branch neural networks for image text matching tasks"

Connected through hyperedge HE-7 (induced by source paper "weakly supervised gaussian networks for action detection"):
  with members:
    - "w talc weakly supervised temporal activity localization and classification"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "weakly supervised action segmentation with iterative soft boundary assignment"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "action sets weakly supervised action segmentation without ordering constraints"
    - "what actions are needed for understanding human actions in videos"
    - "discriminatively learned hierarchical rank pooling networks"

Connected through hyperedge HE-8 (induced by source paper "weakly supervised video moment retrieval from text queries"):
  with members:
    - "w talc weakly supervised temporal activity localization and classification"
    - "multilevel language and vision integration for text to clip retrieval"
    - "webly supervised joint embedding for cross modal image text retrieval"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "learning two branch neural networks for image text matching tasks"
    - "dual path convolutional image text embedding with instance loss"
    - "temporal action detection with structured segment networks"
    - "untrimmednets for weakly supervised action recognition and detection"

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)