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 "zero shot feature selection via exploiting semantic knowledge"):
  Abstract: Feature selection plays an important role in pattern recognition and machine learning systems. Supervised knowledge can significantly improve the performance. However, confronted with the rapid growth of newly-emerging concepts, existing supervised methods may easily suffer from the scarcity of labeled data for training. Therefore, this paper studies the problem of Zero-Shot Feature Selection, i.e., building a feature selection model that generalizes well to "unseen" concepts with limited training data of "seen" concepts. To address this, inspired by zero-shot learning, we use class-semantic descriptions (i.e., attributes) which provide additional semantic information about unseen concepts as supervision. In addition, to seek for more reliable discriminative features, we further propose a novel loss function (named center-characteristic loss) which encourages the selected features to capture the central characteristics of seen concepts. Experimental results on three benchmarks demonstrate the superiority of the proposed method.

Includes members:
  Member "less is more zero shot learning from online textual documents with noise suppression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "variational hetero encoder randomized gans for joint image text modeling")
  Member "multi cue zero shot learning with strong supervision":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "crl class representative learning for image classification")
  Member "unsupervised feature selection with adaptive structure learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi participant multi class vertical federated learning")
  Member "zero shot recognition with unreliable attributes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "latent embedding feedback and discriminative features for zero shot classification")

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)