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: "a mathematical theory of semantic development in deep neural networks"
  Abstract: An extensive body of empirical research has revealed remarkable regularities in the acquisition, organization, deployment, and neural representation of human semantic knowledge, thereby raising a fundamental conceptual question: what are the theoretical principles governing the ability of neural networks to acquire, organize, and deploy abstract knowledge by integrating across many individual experiences? We address this question by mathematically analyzing the nonlinear dynamics of learning in deep linear networks. We find exact solutions to this learning dynamics that yield a conceptual explanation for the prevalence of many disparate phenomena in semantic cognition, including the hierarchical differentiation of concepts through rapid developmental transitions, the ubiquity of semantic illusions between such transitions, the emergence of item typicality and category coherence as factors controlling the speed of semantic processing, changing patterns of inductive projection over development, and the conservation of semantic similarity in neural representations across species. Thus, surprisingly, our simple neural model qualitatively recapitulates many diverse regularities underlying semantic development, while providing analytic insight into how the statistical structure of an environment can interact with nonlinear deep learning dynamics to give rise to these regularities.

Connected through hyperedge HE-1 (induced by source paper "a sample complexity separation between non convex and convex meta learning"):
  with members:
    - "implicit regularization of discrete gradient dynamics in linear neural networks"
    - "decoupling adaptation from modeling with meta optimizers for meta learning"
    - "adaptive gradient based meta learning methods"
    - "learning to learn stochastic gradient descent with biased regularization"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "meta learning with implicit gradients"
    - "on the convergence theory of gradient based model agnostic meta learning algorithms"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-2 (induced by source paper "directional adversarial training for cost sensitive deep learning classification applications"):
  with members:
    - "securing future autonomous connected vehicles challenges posed by adversarial machine learning and the way forward"
    - "analyzing adversarial attacks against deep learning for intrusion detection in iot networks"
    - "robustness via curvature regularization and vice versa"
    - "adversarial logit pairing"
    - "adversarial vulnerability for any classifier"
    - "adversarially robust generalization requires more data"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "label refinery improving imagenet classification through label progression"

Connected through hyperedge HE-3 (induced by source paper "characterizing the shape of activation space in deep neural networks"):
  with members:
    - "topology of learning in artificial neural networks"
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "adversarial examples a complete characterisation of the phenomenon"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "detecting adversarial perturbations through spatial behavior in activation spaces"

Connected through hyperedge HE-4 (induced by source paper "implicit regularization of discrete gradient dynamics in linear neural networks"):
  with members:
    - "implicit bias of gradient descent on linear convolutional networks"
    - "step size matters in deep learning"
    - "on the learning dynamics of deep neural networks"
    - "implicit regularization in deep learning"
    - "geometry of optimization and implicit regularization in deep learning"
    - "algorithmic regularization in over parameterized matrix sensing and neural networks with quadratic activations"
    - "understanding deep learning requires rethinking generalization"
    - "path sgd path normalized optimization in deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "generative continual concept learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "complementary learning for overcoming catastrophic forgetting using experience replay"
    - "few shot adversarial domain adaptation"
    - "prototypical networks for few shot learning"
    - "continual learning with deep generative replay"

Connected through hyperedge HE-6 (induced by source paper "shared representational geometry across neural networks"):
  with members:
    - "identity mappings in deep residual networks"
    - "convergent learning do different neural networks learn the same representations"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"

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)