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: "visualizing natural language descriptions a survey"
  Abstract: A natural language interface exploits the conceptual simplicity and naturalness of the language to create a high-level user-friendly communication channel between humans and machines. One of the promising applications of such interfaces is generating visual interpretations of semantic content of a given natural language that can be then visualized either as a static scene or a dynamic animation. This survey discusses requirements and challenges of developing such systems and reports 26 graphical systems that exploit natural language interfaces and addresses both artificial intelligence and visualization aspects. This work serves as a frame of reference to researchers and to enable further advances in the field.

Connected through hyperedge HE-1 (induced by source paper "generating animations from screenplays"):
  with members:
    - "generating images from captions with attention"

Connected through hyperedge HE-2 (induced by source paper "learning a bidirectional mapping between human whole body motion and natural language using deep recurrent neural networks"):
  with members:
    - "deep representation learning for human motion prediction and classification"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "the kit motion language dataset"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "recurrent batch normalization"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "structural rnn deep learning on spatio temporal graphs"

Connected through hyperedge HE-3 (induced by source paper "encoding spatial relations from natural language"):
  with members:
    - "neural machine translation in linear time"
    - "draw a recurrent neural network for image generation"

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.CL(Computation and Language)