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: "computational role of eccentricity dependent cortical magnification"
  Abstract: We develop a sampling extension of M-theory focused on invariance to scale and translation. Quite surprisingly, the theory predicts an architecture of early vision with increasing receptive field sizes and a high resolution fovea ‐ in agreement with data about the cortical magnification factor, V1 and the retina. From the slope of the inverse of the magnification factor, M-theory predicts a cortical “fovea” in V1 in the order of 40 by 40 basic units at each receptive field size ‐ corresponding to a foveola of size around 26 minutes of arc at the highest resolution, 6 degrees at the lowest resolution. It also predicts uniform scale invariance over a fixed range of scales independently of eccentricity, while translation invariance should depend linearly on spatial frequency. ≈ (B)

Connected through hyperedge HE-1 (induced by source paper "large field and high resolution detecting needle in haystack"):
  with members:
    - "deep learning and quantum entanglement fundamental connections with implications to network design"
    - "inductive bias of deep convolutional networks through pooling geometry"
    - "joint object and part segmentation using deep learned potentials"
    - "attention to scale scale aware semantic image segmentation"
    - "zoom better to see clearer human and object parsing with hierarchical auto zoom net"
    - "on the expressive power of deep learning a tensor analysis"
    - "very deep convolutional networks for large scale image recognition"
    - "recurrent models of visual attention"

Connected through hyperedge HE-2 (induced by source paper "do deep neural networks suffer from crowding"):
  with members:
    - "foveation based mechanisms alleviate adversarial examples"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "view tolerant face recognition and hebbian learning imply mirror symmetric neural tuning to head orientation"):
  with members:
    - "train faster generalize better stability of stochastic gradient descent"
    - "on invariance and selectivity in representation learning"
    - "unsupervised learning of invariant representations in hierarchical architectures"

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.LG(Machine Learning)