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 "learning for new visual environments with limited labels"):
  Abstract: In computer vision applications, such as domain adaptation (DA), few shot learning (FSL) and zero-shot learning (ZSL), we encounter new objects and environments, for which insufficient examples exist to allow for training "models from scratch," and methods that adapt existing models, trained on the presented training environment, to the new scenario are required. We propose a novel visual attribute encoding method that encodes each image as a low-dimensional probability vector composed of prototypical part-type probabilities. The prototypes are learnt to be representative of all training data. At test-time we utilize this encoding as an input to a classifier. At test-time we freeze the encoder and only learn/adapt the classifier component to limited annotated labels in FSL; new semantic attributes in ZSL. We conduct extensive experiments on benchmark datasets. Our method outperforms state-of-art methods trained for the specific contexts (ZSL, FSL, DA).

Includes members:
  Member "multi modal ensemble classification for generalized zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning classifiers for domain adaptation zero and few shot recognition based on learning latent semantic parts")
  Member "domain invariant projection learning for zero shot recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "meta learning across meta tasks for few shot learning")
  Member "zero shot recognition via semantic embeddings and knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "generative dual adversarial network for generalized zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "latent embedding feedback and discriminative features for zero shot classification")
  Member "meta learning with latent embedding optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "revisiting meta learning as supervised learning")
  Member "generalized zero and few shot learning via aligned variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "latent embedding feedback and discriminative features for zero shot classification")
  Member "improved techniques for adversarial discriminative domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "entropy minimization vs diversity maximization for domain adaptation")
  Member "discriminative learning of latent features for zero shot recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an optimal transport framework for zero shot 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)