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: "neuralnetwork viterbi a framework for weakly supervised video learning"
  Abstract: Video learning is an important task in computer vision and has experienced increasing interest over the recent years. Since even a small amount of videos easily comprises several million frames, methods that do not rely on a frame-level annotation are of special importance. In this work, we propose a novel learning algorithm with a Viterbi-based loss that allows for online and incremental learning of weakly annotated video data. We moreover show that explicit context and length modeling leads to huge improvements in video segmentation and labeling tasks andinclude these models into our framework. On several action segmentation benchmarks, we obtain an improvement of up to 10% compared to current state-of-the-art methods.

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"
    - "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"
    - "temporal gaussian mixture layer for videos"

Connected through hyperedge HE-2 (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"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "unsupervised learning and segmentation of complex activities from video"
    - "a perceptual prediction framework for self supervised event segmentation"
    - "weakly supervised action learning with rnn based fine to coarse modeling"

Connected through hyperedge HE-3 (induced by source paper "set constrained viterbi for set supervised action segmentation"):
  with members:
    - "weakly supervised energy based learning for action segmentation"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "w talc weakly supervised temporal activity localization and classification"
    - "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-4 (induced by source paper "learning video representations using contrastive bidirectional transformer"):
  with members:
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "lxmert learning cross modality encoder representations from transformers"
    - "contrastive multiview coding"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "learning correspondence from the cycle consistency of time"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "data efficient image recognition with contrastive predictive coding"
    - "bert has a mouth and it must speak bert as a markov random field language model"

Connected through hyperedge HE-5 (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"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "deep learning is robust to massive label noise"

Connected through hyperedge HE-6 (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"
    - "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"

Connected through hyperedge HE-7 (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"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "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"

Connected through hyperedge HE-8 (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"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "group normalization"
    - "spatio temporal channel correlation networks for action classification"
    - "slowfast networks for video recognition"
    - "weakly supervised action segmentation with iterative soft boundary assignment"

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)