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: "anticipation in human robot cooperation a recurrent neural network approach for multiple action sequences prediction"
  Abstract: Close human-robot cooperation is a key enabler for new developments in advanced manufacturing and assistive applications. Close cooperation require robots that can predict human actions and intent, understanding human non-verbal cues. Recent approaches based on neural networks have led to encouraging results in the human action prediction problem both in continuous and discrete spaces. Our approach extends the research in this direction. Our contributions are three-fold. First, we validate the use of gaze and body pose cues as a means of predicting human action through a feature selection method. Next, we address two shortcomings of existing literature: predicting multiple and variable-length action sequences. This is achieved by applying an encoder-decoder recurrent neural network topology in the discrete action prediction problem. In addition, we theoretically demonstrate the importance of predicting multiple action sequences as a means of estimating the stochastic reward in a human robot cooperation scenario. Finally, we show the ability to effectively train the prediction model on an action prediction dataset, involving human motion data, and explore the influence of the model's parameters on its performance.

Connected through hyperedge HE-1 (induced by source paper "action anticipation for collaborative environments the impact of contextual information and uncertainty based prediction"):
  with members:
    - "delving into 3d action anticipation from streaming videos"
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "early action prediction with generative adversarial networks"
    - "anticipation and next action forecasting in video an end to end model with memory"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "human action recognition and prediction a survey"
    - "action anticipation by predicting future dynamic images"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "action agnostic human pose forecasting"):
  with members:
    - "predicting deeper into the future of semantic segmentation"
    - "hp gan probabilistic 3d human motion prediction via gan"
    - "learning human motion models for long term predictions"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "deep representation learning for human motion prediction and classification"
    - "forecasting human dynamics from static images"
    - "human pose forecasting via deep markov models"
    - "a simple yet effective baseline for 3d human pose estimation"

Connected through hyperedge HE-3 (induced by source paper "action anticipation reading the intentions of humans and robots"):
  with members:
    - "efficient model learning from joint action demonstrations for human robot collaborative tasks"
    - "pupil an open source platform for pervasive eye tracking and mobile gaze based interaction"

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.HC(Human-Computer Interaction)