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: "inductive bias of deep convolutional networks through pooling geometry"
  Abstract: Our formal understanding of the inductive bias that drives the success of convolutional networks on computer vision tasks is limited. In particular, it is unclear what makes hypotheses spaces born from convolution and pooling operations so suitable for natural images. In this paper we study the ability of convolutional networks to model correlations among regions of their input. We theoretically analyze convolutional arithmetic circuits, and empirically validate our findings on other types of convolutional networks as well. Correlations are formalized through the notion of separation rank, which for a given partition of the input, measures how far a function is from being separable. We show that a polynomially sized deep network supports exponentially high separation ranks for certain input partitions, while being limited to polynomial separation ranks for others. The network's pooling geometry effectively determines which input partitions are favored, thus serves as a means for controlling the inductive bias. Contiguous pooling windows as commonly employed in practice favor interleaved partitions over coarse ones, orienting the inductive bias towards the statistics of natural images. Other pooling schemes lead to different preferences, and this allows tailoring the network to data that departs from the usual domain of natural imagery. In addition to analyzing deep networks, we show that shallow ones support only linear separation ranks, and by this gain insight into the benefit of functions brought forth by depth - they are able to efficiently model strong correlation under favored partitions of the input.

Connected through hyperedge HE-1 (induced by source paper "tensor decompositions in deep learning"):
  with members:
    - "theoretically expressive and edge aware graph learning"
    - "tucker tensor layer in fully connected neural networks"
    - "a fair comparison of graph neural networks for graph classification"
    - "bayesian tensor factorisation for bottom up hidden tree markov models"
    - "a gentle introduction to deep learning for graphs"
    - "contextual graph markov model a deep and generative approach to graph processing"
    - "how powerful are graph neural networks"
    - "mutan multimodal tucker fusion for visual question answering"

Connected through hyperedge HE-2 (induced by source paper "weight agnostic neural networks"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "continual lifelong learning with neural networks a review"
    - "the evolved transformer"
    - "reinforcement learning for improving agent design"
    - "evaluating the search phase of neural architecture search"
    - "measuring the intrinsic dimension of objective landscapes"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-3 (induced by source paper "a closed form learned pooling for deep classification networks"):
  with members:
    - "making convolutional networks shift invariant again"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "detail preserving pooling in deep networks"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "polar transformer networks"
    - "learnable pooling with context gating for video classification"
    - "learning bag of features pooling for deep convolutional neural networks"
    - "mask r cnn"

Connected through hyperedge HE-4 (induced by source paper "grouped sparse projection"):
  with members:
    - "deephoyer learning sparser neural network with differentiable scale invariant sparsity measures"
    - "learning sparse networks using targeted dropout"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "importance estimation for neural network pruning"
    - "the state of sparsity in deep neural networks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "rethinking the value of network pruning"
    - "efficient dictionary learning with sparseness enforcing projections"

Connected through hyperedge HE-5 (induced by source paper "the lottery ticket hypothesis finding sparse trainable neural networks"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "rethinking the value of network pruning"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep rewiring training very sparse deep networks"
    - "thinet a filter level pruning method for deep neural network compression"
    - "exploring sparsity in recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "conditional prior networks for optical flow"):
  with members:
    - "emergence of invariance and disentanglement in deep representations"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "densenet for dense flow"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "fast optical flow using dense inverse search"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "optical flow estimation using a spatial pyramid network"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"

Connected through hyperedge HE-7 (induced by source paper "modelling identity rules with neural networks"):
  with members:
    - "extrapolation in nlp"
    - "learning inductive biases with simple neural networks"
    - "relational inductive biases deep learning and graph networks"
    - "deep learning a critical appraisal"
    - "measuring abstract reasoning in neural networks"
    - "dynamic routing between capsules"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (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"
    - "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"
    - "computational role of eccentricity dependent cortical magnification"
    - "very deep convolutional networks for large scale image recognition"
    - "recurrent models of visual attention"

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.NE(Neural and Evolutionary Computing)