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: "hirenet a hierarchical attention model for the automatic analysis of asynchronous video job interviews"
  Abstract: New technologies drastically change recruitment techniques. Some research projects aim at designing interactive systems that help candidates practice job interviews. Other studies aim at the automatic detection of social signals (e.g. smile, turn of speech, etc...) in videos of job interviews. These studies are limited with respect to the number of interviews they process, but also by the fact that they only analyze simulated job interviews (e.g. students pretending to apply for a fake position). Asynchronous video interviewing tools have become mature products on the human resources market, and thus, a popular step in the recruitment process. As part of a project to help recruiters, we collected a corpus of more than 7000 candidates having asynchronous video job interviews for real positions and recording videos of themselves answering a set of questions. We propose a new hierarchical attention model called HireNet that aims at predicting the hirability of the candidates as evaluated by recruiters. In HireNet, an interview is considered as a sequence of questions and answers containing salient socials signals. Two contextual sources of information are modeled in HireNet: the words contained in the question and in the job position. Our model achieves better F1-scores than previous approaches for each modality (verbal content, audio and video). Results from early and late multimodal fusion suggest that more sophisticated fusion schemes are needed to improve on the monomodal results. Finally, some examples of moments captured by the attention mechanisms suggest our model could potentially be used to help finding key moments in an asynchronous job interview.

Connected through hyperedge HE-1 (induced by source paper "learning occupational task shares dynamics for the future of work"):
  with members:
    - "when will ai exceed human performance evidence from ai experts"

Connected through hyperedge HE-2 (induced by source paper "from the token to the review a hierarchical multimodal approach to opinion mining"):
  with members:
    - "a multimodal movie review corpus for fine grained opinion mining"
    - "multi attention recurrent network for human communication comprehension"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "attention is all you need"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "an overview of multi task learning in deep neural networks"
    - "mosi multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"

Connected through hyperedge HE-3 (induced by source paper "slices of attention in asynchronous video job interviews"):
  with members:
    - "attention is not explanation"
    - "improving the interpretability of deep neural networks with knowledge distillation"
    - "learn to pay attention"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "axiomatic attribution for deep networks"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "why should i trust you explaining the predictions of any classifier"
    - "understanding neural networks through deep visualization"

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)