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: "unsupervised learning and segmentation of complex activities from video"
  Abstract: This paper presents a new method for unsupervised segmentation of complex activities from video into multiple steps, or sub-activities, without any textual input. We propose an iterative discriminative-generative approach which alternates between discriminatively learning the appearance of sub-activities from the videos' visual features to sub-activity labels and generatively modelling the temporal structure of sub-activities using a Generalized Mallows Model. In addition, we introduce a model for background to account for frames unrelated to the actual activities. Our approach is validated on the challenging Breakfast Actions and Inria Instructional Videos datasets and outperforms both unsupervised and weakly-supervised state of the art.

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"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "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 "unsupervised temporal video segmentation as an auxiliary task for predicting the remaining surgery duration"):
  with members:
    - "rsdnet learning to predict remaining surgery duration from laparoscopic videos without manual annotations"
    - "unsupervised learning of action classes with continuous temporal embedding"
    - "temporal coherence based self supervised learning for laparoscopic workflow analysis"
    - "less is more surgical phase recognition with less annotations through self supervised pre training of cnn lstm networks"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"
    - "slow and steady feature analysis higher order temporal coherence in video"

Connected through hyperedge HE-3 (induced by source paper "howto100m learning a text video embedding by watching hundred million narrated video clips"):
  with members:
    - "cross task weakly supervised learning from instructional videos"
    - "coin a large scale dataset for comprehensive instructional video analysis"
    - "scaling egocentric vision the epic kitchens dataset"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "a joint sequence fusion model for video question answering and retrieval"
    - "exploring the limits of weakly supervised pretraining"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "temporal cycle consistency learning"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "action completion a temporal model for moment detection"
    - "playing hard exploration games by watching youtube"
    - "neighbourhood consensus networks"
    - "deep unsupervised learning of visual similarities"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "cycada cycle consistent adversarial domain adaptation"
    - "temporal action detection with structured segment networks"

Connected through hyperedge HE-5 (induced by source paper "unsupervised learning of action classes with continuous temporal embedding"):
  with members:
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "spatio temporal channel correlation networks for action classification"
    - "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"
    - "generalized rank pooling for activity recognition"
    - "unsupervised video understanding by reconciliation of posture similarities"
    - "untrimmednets for weakly supervised action recognition and detection"

Connected through hyperedge HE-6 (induced by source paper "cross task weakly supervised learning from instructional videos"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised visual linguistic reference resolution in instructional videos"
    - "joint discovery of object states and manipulation actions"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "action sets weakly supervised action segmentation without ordering constraints"
    - "from lifestyle vlogs to everyday interactions"

Connected through hyperedge HE-7 (induced by source paper "robot learning and execution of collaborative manipulation plans from youtube videos"):
  with members:
    - "procedure planning in instructional videos"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "recipe1m a dataset for learning cross modal embeddings for cooking recipes and food images"
    - "yolov3 an incremental improvement"
    - "functional object oriented network construction expansion"
    - "translating videos to commands for robotic manipulation with deep recurrent neural networks"
    - "learning social affordance grammar from videos transferring human interactions to human robot interactions"
    - "towards automatic learning of procedures from web instructional videos"

Connected through hyperedge HE-8 (induced by source paper "zero shot anticipation for instructional activities"):
  with members:
    - "the limits and potentials of deep learning for robotics"
    - "food recognition and recipe analysis integrating visual content context and external knowledge"
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "end to end dense video captioning with masked transformer"
    - "when will you do what anticipating temporal occurrences of activities"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "towards automatic learning of procedures from web instructional videos"

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)