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 "novelty prepared few shot classification"):
  Abstract: Few-shot classification algorithms can alleviate the data scarceness issue, which is vital in many real-world problems, by adopting models pre-trained from abundant data in other domains. However, the pre-training process was commonly unaware of the future adaptation to other concept classes. We disclose that a classically fully trained feature extractor can leave little embedding space for unseen classes, which keeps the model from well-fitting the new classes. In this work, we propose to use a novelty-prepared loss function, called self-compacting softmax loss (SSL), for few-shot classification. The SSL can prevent the full occupancy of the embedding space. Thus the model is more prepared to learn new classes. In experiments on CUB-200-2011 and mini-ImageNet datasets, we show that SSL leads to significant improvement of the state-of-the-art performance. This work may shed some light on considering the model capacity for few-shot classification tasks.

Includes members:
  Member "deep high resolution representation learning for visual recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mti net multi scale task interaction networks for multi task learning")
  Member "a closer look at few shot classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "revisiting meta learning as supervised learning")
  Member "learning to learn via self critique":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to impute a general framework for semi supervised learning")
  Member "deep high resolution representation learning for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "few shot learning with metric agnostic conditional embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cross domain few shot classification via learned feature wise transformation")
  Member "meta learning with latent embedding optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "revisiting meta learning as supervised learning")
  Member "learning embedding adaptation for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "revisiting meta learning as supervised learning")
  Member "deep comparison relation columns for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "neural snowball for few shot relation 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.LG(Machine Learning)