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: "human ai collaboration in data science exploring data scientists perceptions of automated ai"
  Abstract: The rapid advancement of artificial intelligence (AI) is changing our lives in many ways. One application domain is data science. New techniques in automating the creation of AI, known as AutoAI or AutoML, aim to automate the work practices of data scientists. AutoAI systems are capable of autonomously ingesting and pre-processing data, engineering new features, and creating and scoring models based on a target objectives (e.g. accuracy or run-time efficiency). Though not yet widely adopted, we are interested in understanding how AutoAI will impact the practice of data science. We conducted interviews with 20 data scientists who work at a large, multinational technology company and practice data science in various business settings. Our goal is to understand their current work practices and how these practices might change with AutoAI. Reactions were mixed: while informants expressed concerns about the trend of automating their jobs, they also strongly felt it was inevitable. Despite these concerns, they remained optimistic about their future job security due to a view that the future of data science work will be a collaboration between humans and AI systems, in which both automation and human expertise are indispensable.

Connected through hyperedge HE-1 (induced by source paper "trust in automl exploring information needs for establishing trust in automated machine learning systems"):
  with members:
    - "effect of confidence and explanation on accuracy and trust calibration in ai assisted decision making"
    - "questioning the ai informing design practices for explainable ai user experiences"
    - "one explanation does not fit all a toolkit and taxonomy of ai explainability techniques"
    - "an admm based framework for automl pipeline configuration"
    - "conditional parallel coordinates"
    - "atmseer increasing transparency and controllability in automated machine learning"
    - "survey on automated machine learning"
    - "metrics for explainable ai challenges and prospects"

Connected through hyperedge HE-2 (induced by source paper "autoaiviz opening the blackbox of automated artificial intelligence with conditional parallel coordinates"):
  with members:
    - "effect of confidence and explanation on accuracy and trust calibration in ai assisted decision making"
    - "how do data science workers collaborate roles workflows and tools"
    - "questioning the ai informing design practices for explainable ai user experiences"
    - "model cards for model reporting"
    - "how data scientistswork together with domain experts in scientific collaborations to find the right answer or to ask the right question"
    - "an admm based framework for automl pipeline configuration"
    - "conditional parallel coordinates"
    - "atmseer increasing transparency and controllability in automated 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.HC(Human-Computer Interaction)