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 "prototypical clustering networks for dermatological disease diagnosis"):
  Abstract: We consider the problem of image classification for the purpose of aiding doctors in dermatological diagnosis. Dermatological diagnosis poses two major challenges for standard off-the-shelf techniques: First, the data distribution is typically extremely long tailed. Second, intra-class variability is often large. To address the first issue, we formulate the problem as low-shot learning, where once deployed, a base classifier must rapidly generalize to diagnose novel conditions given very few labeled examples. To model diverse classes effectively, we propose Prototypical Clustering Networks (PCN), an extension to Prototypical Networks that learns a mixture of prototypes for each class. Prototypes are initialized for each class via clustering and refined via an online update scheme. Classification is performed by measuring similarity to a weighted combination of prototypes within a class, where the weights are the inferred cluster responsibilities. We demonstrate the strengths of our approach in effective diagnosis on a realistic dataset of dermatological conditions.

Includes members:
  Member "gaussian prototypical networks for few shot learning on omniglot":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "variational metric scaling for metric based meta learning")
  Member "a systematic study of the class imbalance problem in convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "identifying and compensating for feature deviation in imbalanced deep learning")
  Member "the devil is in the tails fine grained classification in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "identifying and compensating for feature deviation in imbalanced deep learning")
  Member "prototypical networks for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "one shot learning with memory augmented neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "incremental few shot object detection")
  Member "densely connected convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lossless compression of deep neural networks")
  Member "matching networks for one shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "low shot visual recognition by shrinking and hallucinating features":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "revisiting meta learning as supervised learning")

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.CV(Computer Vision and Pattern Recognition)