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: "collaborative human ai chai evidence based interpretable melanoma classification in dermoscopic images"
  Abstract: Automated dermoscopic image analysis has witnessed rapid growth in diagnostic performance. Yet adoption faces resistance, in part, because no evidence is provided to support decisions. In this work, an approach for evidence-based classification is presented. A feature embedding is learned with CNNs, triplet-loss, and global average pooling, and used to classify via kNN search. Evidence is provided as both the discovered neighbors, as well as localized image regions most relevant to measuring distance between query and neighbors. To ensure that results are relevant in terms of both label accuracy and human visual similarity for any skill level, a novel hierarchical triplet logic is implemented to jointly learn an embedding according to disease labels and non-expert similarity. Results are improved over baselines trained on disease labels alone, as well as standard multiclass loss. Quantitative relevance of results, according to non-expert similarity, as well as localized image regions, are also significantly improved.

Connected through hyperedge HE-1 (induced by source paper "teaching ai to explain its decisions using embeddings and multi task learning"):
  with members:
    - "ted teaching ai to explain its decisions"
    - "methods for interpreting and understanding deep neural networks"
    - "boolean decision rules via column generation"
    - "teaching meaningful explanations"
    - "explanation in artificial intelligence insights from the social sciences"
    - "a unified approach to interpreting model predictions"
    - "a formal framework to characterize interpretability of procedures"
    - "accountability of ai under the law the role of explanation"

Connected through hyperedge HE-2 (induced by source paper "teaching meaningful explanations"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"
    - "explanation in artificial intelligence insights from the social sciences"
    - "a formal framework to characterize interpretability of procedures"
    - "challenges for transparency"
    - "accountability of ai under the law the role of explanation"
    - "explainable ai beware of inmates running the asylum or how i learnt to stop worrying and love the social and behavioural sciences"

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.CV(Computer Vision and Pattern Recognition)