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 "gaze embeddings for zero shot image classification"):
  Abstract: Zero-shot image classification using auxiliary information, such as attributes describing discriminative object properties, requires time-consuming annotation by domain experts. We instead propose a method that relies on human gaze as auxiliary information, exploiting that even non-expert users have a natural ability to judge class membership. We present a data collection paradigm that involves a discrimination task to increase the information content obtained from gaze data. Our method extracts discriminative descriptors from the data and learns a compatibility function between image and gaze using three novel gaze embeddings: Gaze Histograms (GH), Gaze Features with Grid (GFG) and Gaze Features with Sequence (GFS). We introduce two new gaze-annotated datasets for fine-grained image classification and show that human gaze data is indeed class discriminative, provides a competitive alternative to expert-annotated attributes, and outperforms other baselines for zero-shot image classification.

Includes members:
  Member "seeing with humans gaze assisted neural image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the fluidity of concept representations in human brain signals")
  Member "label embedding for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "crl class representative learning for image classification")
  Member "latent embeddings for zero shot classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "crl class representative learning for image classification")
  Member "evaluation of output embeddings for fine grained image classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "variational hetero encoder randomized gans for joint image text modeling")
  Member "gazedpm early integration of gaze information in deformable part models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a self validation network for object level human attention estimation")
  Member "prediction of search targets from fixations in open world settings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a self validation network for object level human attention estimation")
  Member "going deeper with convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "multiple instance reinforcement learning for efficient weakly supervised detection in images":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "tree structured reinforcement learning for sequential object localization")

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)