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 "set transformer a framework for attention based permutation invariant neural networks"):
  Abstract: Many machine learning tasks such as multiple instance learning, 3D shape recognition, and few-shot image classification are defined on sets of instances. Since solutions to such problems do not depend on the order of elements of the set, models used to address them should be permutation invariant. We present an attention-based neural network module, the Set Transformer, specifically designed to model interactions among elements in the input set. The model consists of an encoder and a decoder, both of which rely on attention mechanisms. In an effort to reduce computational complexity, we introduce an attention scheme inspired by inducing point methods from sparse Gaussian process literature. It reduces the computation time of self-attention from quadratic to linear in the number of elements in the set. We show that our model is theoretically attractive and we evaluate it on a range of tasks, demonstrating the state-of-the-art performance compared to recent methods for set-structured data.

Includes members:
  Member "robust attentional aggregation of deep feature sets for multi view 3d reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pointloc deep pose regressor for lidar point cloud localization")
  Member "on the limitations of representing functions on sets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on learning sets of symmetric elements")
  Member "attentive neural processes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "local nonparametric meta learning")
  Member "attention based deep multiple instance learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction")
  Member "conditional neural processes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "local nonparametric meta learning")
  Member "deep sets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stochastic latent residual video prediction")
  Member "attend and interact higher order object interactions for video understanding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ego topo environment affordances from egocentric video")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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.LG(Machine Learning)