human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "what do we need to build explainable ai systems for the medical domain"):
  Abstract: Artificial intelligence (AI) generally and machine learning (ML) specifically demonstrate impressive practical success in many different application domains, e.g. in autonomous driving, speech recognition, or recommender systems. Deep learning approaches, trained on extremely large data sets or using reinforcement learning methods have even exceeded human performance in visual tasks, particularly on playing games such as Atari, or mastering the game of Go. Even in the medical domain there are remarkable results. The central problem of such models is that they are regarded as black-box models and even if we understand the underlying mathematical principles, they lack an explicit declarative knowledge representation, hence have difficulty in generating the underlying explanatory structures. This calls for systems enabling to make decisions transparent, understandable and explainable. A huge motivation for our approach are rising legal and privacy aspects. The new European General Data Protection Regulation entering into force on May 25th 2018, will make black-box approaches difficult to use in business. This does not imply a ban on automatic learning approaches or an obligation to explain everything all the time, however, there must be a possibility to make the results re-traceable on demand. In this paper we outline some of our research topics in the context of the relatively new area of explainable-AI with a focus on the application in medicine, which is a very special domain. This is due to the fact that medical professionals are working mostly with distributed heterogeneous and complex sources of data. In this paper we concentrate on three sources: images, *omics data and text. We argue that research in explainable-AI would generally help to facilitate the implementation of AI/ML in the medical domain, and specifically help to facilitate transparency and trust.

Includes members:
  Member "cross modal deep metric learning with multi task regularization":
    also connected through other hyperedges:
      (none)
  Member "towards the augmented pathologist challenges of explainable ai in digital pathology":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the need for speed of ai applications performance comparison of native vs browser based algorithm implementations")
  Member "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on contextual embeddings")
  Member "what does explainable ai really mean a new conceptualization of perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "directions for explainable knowledge enabled systems")
  Member "deep learning for ontology reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "explainable deep rdfs reasoner")
  Member "a glass box interactive machine learning approach for solving np hard problems with the human in the loop":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "explainable failure predictions with rnn classifiers based on time series data")
  Member "generating visual explanations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "explaining with counter visual attributes and examples")
  Member "making the v in vqa matter elevating the role of image understanding in visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "in defense of grid features for visual question answering")

Question: Please tell me which class the source paper 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)