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: "learning a bidirectional mapping between human whole body motion and natural language using deep recurrent neural networks"
  Abstract: Abstract   Linking human whole-body motion and natural language is of great interest for the generation of semantic representations of observed human behaviors as well as for the generation of robot behaviors based on natural language input. While there has been a large body of research in this area, most approaches that exist today require a symbolic representation of motions (e.g. in the form of motion primitives), which have to be defined a-priori or require complex segmentation algorithms. In contrast, recent advances in the field of neural networks and especially deep learning have demonstrated that sub-symbolic representations that can be learned end-to-end usually outperform more traditional approaches, for applications such as machine translation. In this paper we propose a generative model that learns a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks (RNNs) and sequence-to-sequence learning. Our approach does not require any segmentation or manual feature engineering and learns a distributed representation, which is shared for all motions and descriptions. We evaluate our approach on 2  846 human whole-body motions and 6  187 natural language descriptions thereof from the KIT Motion-Language Dataset. Our results clearly demonstrate the effectiveness of the proposed model: We show that our model generates a wide variety of realistic motions only from descriptions thereof in form of a single sentence. Conversely, our model is also capable of generating correct and detailed natural language descriptions from human motions.

Connected through hyperedge HE-1 (induced by source paper "v2cnet a deep learning framework to translate videos to commands for robotic manipulation"):
  with members:
    - "object captioning and retrieval with natural language"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "semantic decomposition and recognition of long and complex manipulation action sequences"
    - "real time 6dof pose relocalization for event cameras with stacked spatial lstm networks"
    - "translating videos to commands for robotic manipulation with deep recurrent neural networks"
    - "one shot visual imitation learning via meta learning"
    - "third person imitation learning"
    - "affordancenet an end to end deep learning approach for object affordance detection"

Connected through hyperedge HE-2 (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 human motion models for long term predictions"
    - "text2action generative adversarial synthesis from language to action"

Connected through hyperedge HE-3 (induced by source paper "robots learn social skills end to end learning of co speech gesture generation for humanoid robots"):
  with members:
    - "speech driven animation with meaningful behaviors"
    - "image to image translation with conditional adversarial networks"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-4 (induced by source paper "text2action generative adversarial synthesis from language to action"):
  with members:
    - "convolutional pose machines"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "generative adversarial text to image synthesis"
    - "sparseness meets deepness 3d human pose estimation from monocular video"
    - "adam a method for stochastic optimization"
    - "grammar as a foreign language"

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)