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: "improving human machine cooperative visual search with soft highlighting"
  Abstract: Advances in machine learning have produced systems that attain human-level performance on certain visual tasks, e.g., object identification. Nonetheless, other tasks requiring visual expertise are unlikely to be entrusted to machines for some time, e.g., satellite and medical imagery analysis. We describe a human-machine cooperative approach to visual search, the aim of which is to outperform either human or machine acting alone. The traditional route to augmenting human performance with automatic classifiers is to draw boxes around regions of an image deemed likely to contain a target. Human experts typically reject this type of hard highlighting. We propose instead a soft highlighting technique in which the saliency of regions of the visual field is modulated in a graded fashion based on classifier confidence level. We report on experiments with both synthetic and natural images showing that soft highlighting achieves a performance synergy surpassing that attained by hard highlighting.

Connected through hyperedge HE-1 (induced by source paper "assessment of faster r cnn in man machine collaborative search"):
  with members:
    - "adversarial attacks against medical deep learning systems"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "attention allocation aid for visual search"
    - "evaluating visual conversational agents via cooperative human ai games"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "improving small object proposals for company logo detection"
    - "machine teaching a new paradigm for building machine learning systems"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-2 (induced by source paper "on human predictions with explanations and predictions of machine learning models a case study on deception detection"):
  with members:
    - "what can ai do for me evaluating machine learning interpretations in cooperative play"
    - "do explanations make vqa models more predictable to a human"
    - "teaching categories to human learners with visual explanations"
    - "a unified approach to interpreting model predictions"
    - "algorithmic decision making and the cost of fairness"
    - "equality of opportunity in supervised learning"
    - "inherent trade offs in the fair determination of risk scores"
    - "rationalizing neural predictions"

Connected through hyperedge HE-3 (induced by source paper "what can ai do for me evaluating machine learning interpretations in cooperative play"):
  with members:
    - "on human predictions with explanations and predictions of machine learning models a case study on deception detection"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "sanity checks for saliency maps"
    - "evaluating feature importance estimates"
    - "trick me if you can adversarial writing of trivia challenge questions"
    - "beyond word importance contextual decomposition to extract interactions from lstms"
    - "on calibration of modern neural networks"

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.HC(Human-Computer Interaction)