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 "parting with illusions about deep active learning"):
  Abstract: Active learning aims to reduce the high labeling cost involved in training machine learning models on large datasets by efficiently labeling only the most informative samples. Recently, deep active learning has shown success on various tasks. However, the conventional evaluation scheme used for deep active learning is below par. Current methods disregard some apparent parallel work in the closely related fields. Active learning methods are quite sensitive w.r.t. changes in the training procedure like data augmentation. They improve by a large-margin when integrated with semi-supervised learning, but barely perform better than the random baseline. We re-implement various latest active learning approaches for image classification and evaluate them under more realistic settings. We further validate our findings for semantic segmentation. Based on our observations, we realistically assess the current state of the field and propose a more suitable evaluation protocol.

Includes members:
  Member "semi supervised semantic segmentation needs strong high dimensional perturbations":
    also connected through other hyperedges:
      (none)
  Member "semi supervised semantic segmentation with high and low level consistency":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "minor constraint disturbances for deep semi supervised learning")
  Member "rethinking deep active learning using unlabeled data at model training":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "high accuracy and high fidelity extraction of neural networks")
  Member "variational adversarial active learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "task aware variational adversarial active learning")
  Member "consistency based semi supervised active learning towards minimizing labeling budget":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "low budget unsupervised label query through domain alignment enforcement")
  Member "unsupervised data augmentation for consistency training":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation")
  Member "learning loss for active learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "task aware variational adversarial active learning")
  Member "mixmatch a holistic approach to semi supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "domain adaptive ensemble 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)