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 "shaping visual representations with language for few shot classification"):
  Abstract: Language is designed to convey useful information about the world, thus serving as a scaffold for efficient human learning. How can we let language guide representation learning in machine learning models? We explore this question in the setting of few-shot visual classification, proposing models which learn to perform visual classification while jointly predicting natural language task descriptions at train time. At test time, with no language available, we find that these language-influenced visual representations are more generalizable, compared to meta-learning baselines and approaches that explicitly use language as a bottleneck for classification.

Includes members:
  Member "baby steps towards few shot learning with multiple semantics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tafssl task adaptive feature sub space learning for few shot classification")
  Member "using natural language for reward shaping in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "language as a cognitive tool to imagine goals in curiosity driven exploration")
  Member "explain yourself leveraging language models for commonsense reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning from explanations with neural execution tree")
  Member "a closer look at few shot classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "revisiting meta learning as supervised learning")
  Member "adaptive cross modal few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tafssl task adaptive feature sub space learning for few shot classification")
  Member "grounding visual explanations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "explaining with counter visual attributes and examples")
  Member "e snli natural language inference with natural language explanations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning from explanations with neural execution tree")
  Member "learning to understand goal specifications by modelling reward":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "language as a cognitive tool to imagine goals in curiosity driven exploration")

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)