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: "learning inductive biases with simple neural networks"
  Abstract: People use rich prior knowledge about the world in order to efficiently learn new concepts. These priors - also known as "inductive biases" - pertain to the space of internal models considered by a learner, and they help the learner make inferences that go beyond the observed data. A recent study found that deep neural networks optimized for object recognition develop the shape bias (Ritter et al., 2017), an inductive bias possessed by children that plays an important role in early word learning. However, these networks use unrealistically large quantities of training data, and the conditions required for these biases to develop are not well understood. Moreover, it is unclear how the learning dynamics of these networks relate to developmental processes in childhood. We investigate the development and influence of the shape bias in neural networks using controlled datasets of abstract patterns and synthetic images, allowing us to systematically vary the quantity and form of the experience provided to the learning algorithms. We find that simple neural networks develop a shape bias after seeing as few as 3 examples of 4 object categories. The development of these biases predicts the onset of vocabulary acceleration in our networks, consistent with the developmental process in children.

Connected through hyperedge HE-1 (induced by source paper "mutual exclusivity as a challenge for deep neural networks"):
  with members:
    - "lost in machine translation a method to reduce meaning loss"
    - "infinite mixture prototypes for few shot learning"
    - "human few shot learning of compositional instructions"
    - "compositional generalization through meta sequence to sequence learning"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "prototypical networks for few shot learning"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "achieving open vocabulary neural machine translation with hybrid word character models"

Connected through hyperedge HE-2 (induced by source paper "learning a smooth kernel regularizer for convolutional neural networks"):
  with members:
    - "recasting gradient based meta learning as hierarchical bayes"
    - "matching networks for one shot learning"
    - "building machines that learn and think like people"
    - "fast r cnn"
    - "very deep convolutional networks for large scale image recognition"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "modelling identity rules with neural networks"):
  with members:
    - "extrapolation in nlp"
    - "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"
    - "inductive bias of deep convolutional networks through pooling geometry"
    - "adam a method for stochastic optimization"

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.CL(Computation and Language)