human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "balancedness and alignment are unlikely in linear neural networks"):
  Abstract: We study the invariance properties of alignment in linear neural networks under gradient descent. Alignment of weight matrices is a form of implicit regularization, and previous works have studied this phenomenon in fully connected networks with 1-dimensional outputs. In such networks, we prove that there exists an initialization such that adjacent layers remain aligned throughout training under any real-valued loss function. We then define alignment for fully connected networks with multidimensional outputs and prove that it generally cannot be an invariant for such networks under the squared loss. Moreover, we characterize the datasets under which alignment is possible. We then analyze networks with layer constraints such as convolutional networks. In particular, we prove that gradient descent is equivalent to projected gradient descent, and show that alignment is impossible given sufficiently large datasets. Importantly, since our definition of alignment is a relaxation of balancedness, our negative results extend to this property.

Includes members:
  Member "how do infinite width bounded norm networks look in function space":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural networks are convex regularizers exact polynomial time convex optimization formulations for two layer networks")
  Member "memorization in overparameterized autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fixing bias in reconstruction based anomaly detection with lipschitz discriminators")
  Member "global convergence of adaptive gradient methods for an over parameterized neural network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "convergence of end to end training in deep unsupervised contrasitive learning")
  Member "implicit regularization in deep matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "fine grained analysis of optimization and generalization for overparameterized two layer neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self orthogonality module a network architecture plug in for learning orthogonal filters")
  Member "implicit bias of gradient descent on linear convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "gradient descent aligns the layers of deep linear networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the global convergence of training deep linear resnets")

Question: Please tell me which class the source paper 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)