human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "on multi view feature learning"
  Abstract: Sparse coding is a common approach to learning local features for object recognition. Recently, there has been an increasing interest in learning features from spatio-temporal, binocular, or other multi-observation data, where the goal is to encode the relationship between images rather than the content of a single image. We provide an analysis of multi-view feature learning, which shows that hidden variables encode transformations by detecting rotation angles in the eigenspaces shared among multiple image warps. Our analysis helps explain recent experimental results showing that transformation-specific features emerge when training complex cell models on videos. Our analysis also shows that transformation-invariant features can emerge as a by-product of learning representations of transformations.

Connected through hyperedge HE-1 (induced by source paper "modeling musical structure with artificial neural networks"):
  with members:
    - "high level control of drum track generation using learned patterns of rhythmic interaction"
    - "learning complex basis functions for invariant representations of audio"
    - "counterpoint by convolution"
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "a predictive model for music based on learned interval representations"
    - "improving content invariance in gated autoencoders for 2d and 3d object rotation"
    - "text based lstm networks for automatic music composition"
    - "deepbach a steerable model for bach chorales generation"

Connected through hyperedge HE-2 (induced by source paper "high level control of drum track generation using learned patterns of rhythmic interaction"):
  with members:
    - "audio style transfer"
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "deepj style specific music generation"
    - "lead sheet generation and arrangement by conditional generative adversarial network"
    - "learning transposition invariant interval features from symbolic music and audio"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "a predictive model for music based on learned interval representations"
    - "self normalizing neural networks"

Connected through hyperedge HE-3 (induced by source paper "learning transposition invariant interval features from symbolic music and audio"):
  with members:
    - "a predictive model for music based on learned interval representations"
    - "audio to score alignment using transposition invariant features"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-4 (induced by source paper "appearance and relation networks for video classification"):
  with members:
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "activitynet challenge 2017 summary"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "the kinetics human action video dataset"
    - "attention clusters purely attention based local feature integration for video classification"
    - "convnet architecture search for spatiotemporal feature learning"
    - "temporal segment networks towards good practices for deep action recognition"

Connected through hyperedge HE-5 (induced by source paper "learning musical relations using gated autoencoders"):
  with members:
    - "spatial transformer networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "improving content invariance in gated autoencoders for 2d and 3d object rotation"):
  with members:
    - "discovering hidden factors of variation in deep networks"

Connected through hyperedge HE-7 (induced by source paper "gated networks an inventory"):
  with members:
    - "unsupervised learning of video representations using lstms"
    - "continuous control with deep reinforcement learning"
    - "modeling sequential data using higher order relational features and predictive training"
    - "generative class conditional autoencoders"
    - "mental rotation by optimizing transforming distance"
    - "deep learning of representations looking forward"

Connected through hyperedge HE-8 (induced by source paper "transformation properties of learned visual representations"):
  with members:
    - "learning the irreducible representations of commutative lie groups"
    - "learning stable group invariant representations with convolutional networks"
    - "invariant scattering convolution networks"
    - "steps towards a theory of visual information active perception signal to symbol conversion and the interplay between sensing and control"
    - "an unsupervised algorithm for learning lie group transformations"

Question: Please tell me which class the center node 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)