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: "ms tcn multi stage temporal convolutional network for action segmentation"
  Abstract: Temporally locating and classifying action segments in long untrimmed videos is of particular interest to many applications like surveillance and robotics. While traditional approaches follow a two-step pipeline, by generating frame-wise probabilities and then feeding them to high-level temporal models, recent approaches use temporal convolutions to directly classify the video frames. In this paper, we introduce a multi-stage architecture for the temporal action segmentation task. Each stage features a set of dilated temporal convolutions to generate an initial prediction that is refined by the next one. This architecture is trained using a combination of a classification loss and a proposed smoothing loss that penalizes over-segmentation errors. Extensive evaluation shows the effectiveness of the proposed model in capturing long-range dependencies and recognizing action segments. Our model achieves state-of-the-art results on three challenging datasets: 50Salads, Georgia Tech Egocentric Activities (GTEA), and the Breakfast dataset.

Connected through hyperedge HE-1 (induced by source paper "action segmentation with joint self supervised temporal domain adaptation"):
  with members:
    - "temporal attentive alignment for video domain adaptation"
    - "temporal attentive alignment for large scale video domain adaptation"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "attending to discriminative certainty for domain adaptation"
    - "human action recognition and prediction a survey"
    - "learning motion in feature space locally consistent deformable convolution networks for fine grained action detection"
    - "multi adversarial domain adaptation"
    - "self supervised video representation learning with space time cubic puzzles"

Connected through hyperedge HE-2 (induced by source paper "fast video crowd counting with a temporal aware network"):
  with members:
    - "scar spatial channel wise attention regression networks for crowd counting"
    - "learning spatial awareness to improve crowd counting"
    - "learning from synthetic data for crowd counting in the wild"
    - "pcc net perspective crowd counting via spatial convolutional network"
    - "attend to count crowd counting with adaptive capacity multi scale cnns"
    - "crowd counting with deep structured scale integration network"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "adaptive scenario discovery for crowd counting"

Connected through hyperedge HE-3 (induced by source paper "convolutional neural networks for continuous qoe prediction in video streaming services"):
  with members:
    - "modeling continuous video qoe evolution a state space approach"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "streaming video qoe modeling and prediction a long short term memory approach"
    - "self normalizing neural networks"
    - "an augmented autoregressive approach to http video stream quality prediction"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "wavenet a generative model for raw audio"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"

Connected through hyperedge HE-4 (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"
    - "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"
    - "guess where actor supervision for spatiotemporal action localization"

Connected through hyperedge HE-5 (induced by source paper "weakly supervised action segmentation using mutual consistency"):
  with members:
    - "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"
    - "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 "low rank random tensor for bilinear pooling"):
  with members:
    - "local temporal bilinear pooling for fine grained action parsing"
    - "bilinear attention networks"
    - "hierarchical bilinear pooling for fine grained visual recognition"
    - "higher order pooling of cnn features via kernel linearization for action recognition"
    - "mutan multimodal tucker fusion for visual question answering"
    - "is second order information helpful for large scale visual recognition"
    - "attentional pooling for action recognition"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"

Connected through hyperedge HE-7 (induced by source paper "human motion anticipation with symbolic label"):
  with members:
    - "learning trajectory dependencies for human motion prediction"
    - "forecasting future action sequences with neural memory networks"
    - "modeling human motion with quaternion based neural networks"
    - "a neural temporal model for human motion prediction"
    - "quaternet a quaternion based recurrent model for human motion"
    - "convolutional sequence to sequence model for human dynamics"
    - "when will you do what anticipating temporal occurrences of activities"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"

Connected through hyperedge HE-8 (induced by source paper "video crowd counting via dynamic temporal modeling"):
  with members:
    - "crowd counting by adapting convolutional neural networks with side information"
    - "very deep convolutional networks for large scale image recognition"

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)