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: "human action forecasting by learning task grammars"
  Abstract: For effective human-robot interaction, it is important that a robotic assistant can forecast the next action a human will consider in a given task. Unfortunately, real-world tasks are often very long, complex, and repetitive; as a result forecasting is not trivial. In this paper, we propose a novel deep recurrent architecture that takes as input features from a two-stream Residual action recognition framework, and learns to estimate the progress of human activities from video sequences -- this surrogate progress estimation task implicitly learns a temporal task grammar with respect to which activities can be localized and forecasted. To learn the task grammar, we propose a stacked LSTM based multi-granularity progress estimation framework that uses a novel cumulative Euclidean loss as objective. To demonstrate the effectiveness of our proposed architecture, we showcase experiments on two challenging robotic assistive tasks, namely (i) assembling an Ikea table from its constituents, and (ii) changing the tires of a car. Our results demonstrate that learning task grammars offers highly discriminative cues improving the forecasting accuracy by more than 9% over the baseline two-stream forecasting model, while also outperforming other competitive schemes.

Connected through hyperedge HE-1 (induced by source paper "ikea furniture assembly environment for long horizon complex manipulation tasks"):
  with members:
    - "to follow or not to follow selective imitation learning from observations"
    - "doorgym a scalable door opening environment and baseline agent"
    - "monet unsupervised scene decomposition and representation"
    - "rlbench the robot learning benchmark learning environment"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "deepmind control suite"
    - "virtualhome simulating household activities via programs"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-2 (induced by source paper "learning representations for predicting future activities"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "predicting future instance segmentations by forecasting convolutional features"
    - "stochastic adversarial video prediction"
    - "when will you do what anticipating temporal occurrences of activities"
    - "uncertainty estimates and multi hypotheses networks for optical flow"
    - "predicting action tubes"
    - "predicting deeper into the future of semantic segmentation"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-3 (induced by source paper "am i done predicting action progress in videos"):
  with members:
    - "rsdnet learning to predict remaining surgery duration from laparoscopic videos without manual annotations"
    - "action completion a temporal model for moment detection"
    - "guess where actor supervision for spatiotemporal action localization"
    - "temporal recurrent networks for online action detection"
    - "what actions are needed for understanding human actions in videos"
    - "temporal action detection with structured segment networks"
    - "action tubelet detector for spatio temporal action localization"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed 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)