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 "correlated embeddings of pairs of dependent random variables"):
  Abstract: Maximally correlated embeddings of pairs of random variables play a central role in several learning problems. For a fixed data distribution, these embeddings belong to the Hilbert space of finite-variance functions regardless of how they are derived. In this paper, we overview an information-theoretic framework for analyzing this function space, and demonstrate how a basis for this space can be approximated from data using deep neural networks. Together with experiments on different datasets, we show that this framework (i) enables classical exploratory statistical techniques such as correspondence analysis to be performed at scale with continuous variables, (ii) can be used to derive new methods for comparing black-box classification models, and (iii) underlies recent multi-view and multi-modal learning methods.

Includes members:
  Member "on the sample complexity of hgr maximal correlation functions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on universal features for high dimensional learning and inference")
  Member "a survey of multi view representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "contrastive multiview coding")
  Member "an efficient approach to informative feature extraction from multimodal data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the sample complexity of hgr maximal correlation functions")
  Member "deep generalized canonical correlation analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on universal features for high dimensional learning and inference")
  Member "a generalized power iteration method for solving quadratic problem on the stiefel manifold":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrete optimal graph clustering")
  Member "deep variational canonical correlation analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning robust representations via multi view information bottleneck")
  Member "principal inertia components and applications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "task based quantization with application to mimo receivers")
  Member "tensorflow a system for large scale machine learning":
    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)