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 to describe differences between pairs of similar images"
  Abstract: In this paper, we introduce the task of automatically generating text to describe the differences between two similar images. We collect a new dataset by crowd-sourcing difference descriptions for pairs of image frames extracted from video-surveillance footage. Annotators were asked to succinctly describe all the differences in a short paragraph. As a result, our novel dataset provides an opportunity to explore models that align language and vision, and capture visual salience. The dataset may also be a useful benchmark for coherent multi-sentence generation. We perform a firstpass visual analysis that exposes clusters of differing pixels as a proxy for object-level differences. We propose a model that captures visual salience by using a latent variable to align clusters of differing pixels with output sentences. We find that, for both single-sentence generation and as well as multi-sentence generation, the proposed model outperforms the models that use attention alone.

Connected through hyperedge HE-1 (induced by source paper "adjusting image attributes of localized regions with low level dialogue"):
  with members:
    - "a multimodal dialogue system for conversational image editing"
    - "expressing visual relationships via language"
    - "learning to globally edit images with textual description"
    - "contextual slot carryover for disparate schemas"
    - "a corpus for reasoning about natural language grounded in photographs"
    - "a system for automated image editing from natural language commands"
    - "mattnet modular attention network for referring expression comprehension"
    - "sequential attention gan for interactive image editing via dialogue"

Connected through hyperedge HE-2 (induced by source paper "robust change captioning"):
  with members:
    - "clevr dialog a diagnostic dataset for multi round reasoning in visual dialog"
    - "clevr ref diagnosing visual reasoning with referring expressions"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural baby talk"
    - "discriminability objective for training descriptive captions"
    - "explainable neural computation via stack neural module networks"

Connected through hyperedge HE-3 (induced by source paper "expressing visual relationships via language"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "learning to globally edit images with textual description"
    - "discriminability objective for training descriptive captions"
    - "a corpus for reasoning about natural language grounded in photographs"
    - "learning to color from language"
    - "pragmatically informative image captioning with character level reference"
    - "reinforced video captioning with entailment rewards"
    - "improved image captioning via policy gradient optimization of spider"

Connected through hyperedge HE-4 (induced by source paper "fashion iq a new dataset towards retrieving images by natural language feedback"):
  with members:
    - "neural naturalist generating fine grained image comparisons"
    - "robust change captioning"
    - "drill down interactive retrieval of complex scenes using natural language queries"
    - "expressing visual relationships via language"
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "composing text and image for image retrieval an empirical odyssey"
    - "dialog based interactive image retrieval"
    - "person search with natural language description"

Connected through hyperedge HE-5 (induced by source paper "cooperative image captioning"):
  with members:
    - "binary image selection bison interpretable evaluation of visual grounding"
    - "evaluating text to image matching using binary image selection bison"
    - "a neural compositional paradigm for image captioning"
    - "how agents see things on visual representations in an emergent language game"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "natural language does not emerge naturally in multi agent dialog"
    - "speaking the same language matching machine to human captions by adversarial training"

Connected through hyperedge HE-6 (induced by source paper "neural naturalist generating fine grained image comparisons"):
  with members:
    - "robust change captioning"
    - "expressing visual relationships via language"
    - "a corpus for reasoning about natural language grounded in photographs"
    - "attention is all you need"
    - "context aware captions from context agnostic supervision"
    - "the inaturalist species classification and detection dataset"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "generating visual explanations"

Connected through hyperedge HE-7 (induced by source paper "blocksworld revisited learning and reasoning to generate event sequences from image pairs"):
  with members:
    - "modularized textual grounding for counterfactual resilience"
    - "robust change captioning"
    - "large scale visual relationship understanding"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a simple neural network module for relational reasoning"
    - "mask r cnn"
    - "temporal relational reasoning in videos"

Connected through hyperedge HE-8 (induced by source paper "playing log n questions over sentences"):
  with members:
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "learning to disambiguate by asking discriminative questions"
    - "interpretable semantic textual similarity"
    - "constructing datasets for multi hop reading comprehension across documents"
    - "end to end memory networks"
    - "a large annotated corpus for learning natural language inference"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"

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)