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: "text2action generative adversarial synthesis from language to action"
  Abstract: In this paper, we propose a generative model which learns the relationship between language and human action in order to generate a human action sequence given a sentence describing human behavior. The proposed generative model is a generative adversarial network (GAN), which is based on the sequence to sequence (SEQ2SEQ) model. Using the proposed generative network, we can synthesize various actions for a robot or a virtual agent using a text encoder recurrent neural network (RNN) and an action decoder RNN. The proposed generative network is trained from 29,770 pairs of actions and sentence annotations extracted from MSR-Video-to-Text (MSR-VTT), a large-scale video dataset. We demonstrate that the network can generate human-like actions which can be transferred to a Baxter robot, such that the robot performs an action based on a provided sentence. Results show that the proposed generative network correctly models the relationship between language and action and can generate a diverse set of actions from the same sentence.

Connected through hyperedge HE-1 (induced by source paper "language2pose natural language grounded pose forecasting"):
  with members:
    - "generating animations from screenplays"
    - "pose guided human video generation"
    - "human motion modeling using dvgans"
    - "quaternet a quaternion based recurrent model for human motion"
    - "action agnostic human pose forecasting"
    - "long term human motion prediction by modeling motion context and enhancing motion dynamic"
    - "learning a bidirectional mapping between human whole body motion and natural language using deep recurrent neural networks"
    - "learning human motion models for long term predictions"

Connected through hyperedge HE-2 (induced by source paper "generative autoregressive networks for 3d dancing move synthesis from music"):
  with members:
    - "modeling human motion with quaternion based neural networks"
    - "listen to dance music driven choreography generation using autoregressive encoder decoder network"
    - "one deep music representation to rule them all a comparative analysis of different representation learning strategies"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "sample level cnn architectures for music auto tagging using raw waveforms"
    - "wavenet a generative model for raw audio"
    - "generative choreography using deep learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "coloring with words guiding image colorization through text based palette generation"):
  with members:
    - "interactive deep colorization with simultaneous global and local inputs"
    - "attention is all you need"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "pixcolor pixel recursive colorization"
    - "real time user guided image colorization with learned deep priors"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "colors in context a pragmatic neural model for grounded language understanding"
    - "learning deep representations of fine grained visual descriptions"

Connected through hyperedge HE-4 (induced by source paper "human motion modeling using dvgans"):
  with members:
    - "learning human motion models for long term predictions"
    - "deep representation learning for human motion prediction and classification"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "on human motion prediction using recurrent neural networks"
    - "language generation with recurrent generative adversarial networks without pre training"
    - "high resolution image synthesis and semantic manipulation with conditional gans"

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.LG(Machine Learning)