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: "a glass box interactive machine learning approach for solving np hard problems with the human in the loop"
  Abstract: The goal of Machine Learning to automatically learn from data, extract knowledge and to make decisions without any human intervention. Such automatic (aML) approaches show impressive success. Recent results even demonstrate intriguingly that deep learning applied for automatic classification of skin lesions is on par with the performance of dermatologists, yet outperforms the average. As human perception is inherently limited, such approaches can discover patterns, e.g. that two objects are similar, in arbitrarily high-dimensional spaces what no human is able to do. Humans can deal only with limited amounts of data, whilst big data is beneficial for aML; however, in health informatics, we are often confronted with a small number of data sets, where aML suffer of insufficient training samples and many problems are computationally hard. Here, interactive machine learning (iML) may be of help, where a human-in-the-loop contributes to reduce the complexity of NP-hard problems. A further motivation for iML is that standard black-box approaches lack transparency, hence do not foster trust and acceptance of ML among end-users. Rising legal and privacy aspects, e.g. with the new European General Data Protection Regulations, make black-box approaches difficult to use, because they often are not able to explain why a decision has been made. In this paper, we present some experiments to demonstrate the effectiveness of the human-in-the-loop approach, particularly in opening the black-box to a glass-box and thus enabling a human directly to interact with an learning algorithm. We selected the Ant Colony Optimization framework, and applied it on the Traveling Salesman Problem, which is a good example, due to its relevance for health informatics, e.g. for the study of protein folding. From studies of how humans extract so much from so little data, fundamental ML-research also may benefit.

Connected through hyperedge HE-1 (induced by source paper "explainable failure predictions with rnn classifiers based on time series data"):
  with members:
    - "the challenge of crafting intelligible intelligence"
    - "explainable time series tweaking via irreversible and reversible temporal transformations"
    - "lstm fully convolutional networks for time series classification"
    - "anomaly detection using one class neural networks"
    - "interpretable categorization of heterogeneous time series data"
    - "interpretable explorable approximations of black box models"
    - "attention is all you need"
    - "time dependent representation for neural event sequence prediction"

Connected through hyperedge HE-2 (induced by source paper "kandinsky patterns"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "what do we need to build explainable ai systems for the medical domain"
    - "generating visual explanations"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"

Connected through hyperedge HE-3 (induced by source paper "explanation methods in deep learning users values concerns and challenges"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "deep learning a critical appraisal"
    - "audio adversarial examples targeted attacks on speech to text"
    - "beyond word importance contextual decomposition to extract interactions from lstms"
    - "towards interpretable r cnn by unfolding latent structures"
    - "interpretable explorable approximations of black box models"
    - "detecting adversarial attacks on neural network policies with visual foresight"
    - "recurrent relational networks"

Connected through hyperedge HE-4 (induced by source paper "what do we need to build explainable ai systems for the medical domain"):
  with members:
    - "cross modal deep metric learning with multi task regularization"
    - "towards the augmented pathologist challenges of explainable ai in digital pathology"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "what does explainable ai really mean a new conceptualization of perspectives"
    - "deep learning for ontology reasoning"
    - "generating visual explanations"
    - "making the v in vqa matter elevating the role of image understanding in visual question answering"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "towards the augmented pathologist challenges of explainable ai in digital pathology"):
  with members:
    - "augmentor an image augmentation library for machine learning"

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.AI(Artificial Intelligence)