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: "deep embedding for spatial role labeling"
  Abstract: This paper introduces the visually informed embedding of word (VIEW), a continuous vector representation for a word extracted from a deep neural model trained using the Microsoft COCO data set to forecast the spatial arrangements between visual objects, given a textual description. The model is composed of a deep multilayer perceptron (MLP) stacked on the top of a Long Short Term Memory (LSTM) network, the latter being preceded by an embedding layer. The VIEW is applied to transferring multimodal background knowledge to Spatial Role Labeling (SpRL) algorithms, which recognize spatial relations between objects mentioned in the text. This work also contributes with a new method to select complementary features and a fine-tuning method for MLP that improves the $F1$ measure in classifying the words into spatial roles. The VIEW is evaluated with the Task 3 of SemEval-2013 benchmark data set, SpaceEval.

Connected through hyperedge HE-1 (induced by source paper "end to end adversarial learning for generative conversational agents"):
  with members:
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "adversarial learning for neural dialogue generation"
    - "a persona based neural conversation model"
    - "sequence to backward and forward sequences a content introducing approach to generative short text conversation"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "neural contextual conversation learning with labeled question answering pairs"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"

Connected through hyperedge HE-2 (induced by source paper "learning multi modal word representation grounded in visual context"):
  with members:
    - "problems with evaluation of word embeddings using word similarity tasks"
    - "a neural attention model for abstractive sentence summarization"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "show attend and tell neural image caption generation with visual attention"
    - "visual word2vec vis w2v learning visually grounded word embeddings using abstract scenes"
    - "learning better word embedding by asymmetric low rank projection of knowledge graph"
    - "rethinking the inception architecture for computer vision"
    - "combining language and vision with a multimodal skip gram model"

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)