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: "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
  Abstract: We address weakly supervised action alignment and segmentation in videos, where only the order of occurring actions is available during training. We propose Discriminative Differentiable Dynamic Time Warping (D3TW), the first discriminative model using weak ordering supervision. The key technical challenge for discriminative modeling with weak supervision is that the loss function of the ordering supervision is usually formulated using dynamic programming and is thus not differentiable. We address this challenge with a continuous relaxation of the min-operator in dynamic programming and extend the alignment loss to be differentiable. The proposed D3TW innovatively solves sequence alignment with discriminative modeling and end-to-end training, which substantially improves the performance in weakly supervised action alignment and segmentation tasks. We show that our model is able to bypass the degenerated sequence problem usually encountered in previous work and outperform the current state-of-the-art across three evaluation metrics in two challenging datasets.

Connected through hyperedge HE-1 (induced by source paper "joint visual temporal embedding for unsupervised learning of actions in untrimmed sequences"):
  with members:
    - "a hybrid rnn hmm approach for weakly supervised temporal action segmentation"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "weakly supervised energy based learning for action segmentation"
    - "unsupervised learning of action classes with continuous temporal embedding"
    - "unsupervised learning and segmentation of complex activities from video"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "a perceptual prediction framework for self supervised event segmentation"
    - "weakly supervised action learning with rnn based fine to coarse modeling"

Connected through hyperedge HE-2 (induced by source paper "set constrained viterbi for set supervised action segmentation"):
  with members:
    - "weakly supervised energy based learning for action segmentation"
    - "w talc weakly supervised temporal activity localization and classification"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "weakly supervised action segmentation with iterative soft boundary assignment"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "action sets weakly supervised action segmentation without ordering constraints"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "hide and seek forcing a network to be meticulous for weakly supervised object and action localization"

Connected through hyperedge HE-3 (induced by source paper "refineloc iterative refinement for weakly supervised action localization"):
  with members:
    - "3c net category count and center loss for weakly supervised action localization"
    - "slim densepose thrifty learning from sparse annotations and motion cues"
    - "unsupervised pre training of image features on non curated data"
    - "ms tcn multi stage temporal convolutional network for action segmentation"
    - "weakly supervised action localization with background modeling"
    - "bmn boundary matching network for temporal action proposal generation"
    - "deep learning is robust to massive label noise"
    - "guess where actor supervision for spatiotemporal action localization"

Connected through hyperedge HE-4 (induced by source paper "procedure planning in instructional videos"):
  with members:
    - "cross task weakly supervised learning from instructional videos"
    - "videobert a joint model for video and language representation learning"
    - "coin a large scale dataset for comprehensive instructional video analysis"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "universal planning networks"
    - "learning latent dynamics for planning from pixels"
    - "learning plannable representations with causal infogan"
    - "stochastic adversarial video prediction"

Connected through hyperedge HE-5 (induced by source paper "weakly supervised action segmentation using mutual consistency"):
  with members:
    - "ms tcn multi stage temporal convolutional network for action segmentation"
    - "weakly supervised energy based learning for action segmentation"
    - "large scale holistic video understanding"
    - "group normalization"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "spatio temporal channel correlation networks for action classification"
    - "slowfast networks for video recognition"
    - "weakly supervised action segmentation with iterative soft boundary assignment"

Connected through hyperedge HE-6 (induced by source paper "human action sequence classification"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "forecasting future action sequences with neural memory networks"
    - "streamlined dense video captioning"
    - "weakly supervised gaussian networks for action detection"
    - "end to end dense video captioning with masked transformer"
    - "jointly localizing and describing events for dense video captioning"
    - "timeception for complex action recognition"
    - "temporal gaussian mixture layer for videos"

Connected through hyperedge HE-7 (induced by source paper "few shot video classification via temporal alignment"):
  with members:
    - "a closer look at few shot classification"
    - "a generative approach to zero shot and few shot action recognition"
    - "eco efficient convolutional network for online video understanding"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "meta learning with latent embedding optimization"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "videos as space time region graphs"

Connected through hyperedge HE-8 (induced by source paper "learning temporal action proposals with fewer labels"):
  with members:
    - "ctap complementary temporal action proposal generation"
    - "semi supervised sequence modeling with cross view training"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "recurrent ladder networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

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)