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: "from visual attributes to adjectives through decompositional distributional semantics"
  Abstract: As automated image analysis progresses, there is increasing interest in richer linguistic annotation of pictures, with attributes of objects (e.g., furry, brown...) attracting most attention. By building on the recent "zero-shot learning" approach, and paying attention to the linguistic nature of attributes as noun modifiers, and specifically adjectives, we show that it is possible to tag images with attribute-denoting adjectives even when no training data containing the relevant annotation are available. Our approach relies on two key observations. First, objects can be seen as bundles of attributes, typically expressed as adjectival modifiers (a dog is something furry, brown, etc.), and thus a function trained to map visual representations of objects to nominal labels can implicitly learn to map attributes to adjectives. Second, objects and attributes come together in pictures (the same thing is a dog and it is brown). We can thus achieve better attribute (and object) label retrieval by treating images as "visual phrases", and decomposing their linguistic representation into an attribute-denoting adjective and an object-denoting noun. Our approach performs comparably to a method exploiting manual attribute annotation, it outperforms various competitive alternatives in both attribute and object annotation, and it automatically constructs attribute-centric representations that significantly improve performance in supervised object recognition.

Connected through hyperedge HE-1 (induced by source paper "visually grounded situated learning in neural models"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "using the output embedding to improve language models"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "show attend and tell neural image caption generation with visual attention"
    - "rethinking the inception architecture for computer vision"
    - "combining language and vision with a multimodal skip gram model"
    - "unifying visual semantic embeddings with multimodal neural language models"

Connected through hyperedge HE-2 (induced by source paper "like a baby visually situated neural language acquisition"):
  with members:
    - "learning simpler language models with the differential state framework"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "using the output embedding to improve language models"
    - "rethinking the inception architecture for computer vision"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "show and tell a neural image caption generator"

Connected through hyperedge HE-3 (induced by source paper "the focus aspect polarity model for predicting subjective noun attributes in images"):
  with members:
    - "deep cross residual learning for multitask visual recognition"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "visual affect around the world a large scale multilingual visual sentiment ontology"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"

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)