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: "vifidel evaluating the visual fidelity of image descriptions"
  Abstract: We address the task of evaluating image description generation systems. We propose a novel image-aware metric for this task: VIFIDEL. It estimates the faithfulness of a generated caption with respect to the content of the actual image, based on the semantic similarity between labels of objects depicted in images and words in the description. The metric is also able to take into account the relative importance of objects mentioned in human reference descriptions during evaluation. Even if these human reference descriptions are not available, VIFIDEL can still reliably evaluate system descriptions. The metric achieves high correlation with human judgments on two well-known datasets and is competitive with metrics that depend on human references

Connected through hyperedge HE-1 (induced by source paper "reinforcing an image caption generator using off line human feedback"):
  with members:
    - "neural naturalist generating fine grained image comparisons"
    - "informative image captioning with external sources of information"
    - "graph rise graph regularized image semantic embedding"
    - "decoupled box proposal and featurization with ultrafine grained semantic labels improve image captioning and visual question answering"
    - "neural baby talk"
    - "reward learning from human preferences and demonstrations in atari"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "quality estimation for image captions based on large scale human evaluations"):
  with members:
    - "informative image captioning with external sources of information"
    - "graph rise graph regularized image semantic embedding"
    - "decoupled box proposal and featurization with ultrafine grained semantic labels improve image captioning and visual question answering"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "spice semantic propositional image caption evaluation"

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)