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 capacity scaling law for artificial neural networks"
  Abstract: We derive the calculation of two critical numbers predicting the behavior of perceptron networks. First, we derive the calculation of what we call the lossless memory (LM) dimension. The LM dimension is a generalization of the Vapnik--Chervonenkis (VC) dimension that avoids structured data and therefore provides an upper bound for perfectly fitting almost any training data. Second, we derive what we call the MacKay (MK) dimension. This limit indicates a 50% chance of not being able to train a given function. Our derivations are performed by embedding a neural network into Shannon's communication model which allows to interpret the two points as capacities measured in bits. We present a proof and practical experiments that validate our upper bounds with repeatable experiments using different network configurations, diverse implementations, varying activation functions, and several learning algorithms. The bottom line is that the two capacity points scale strictly linear with the number of weights. Among other practical applications, our result allows to compare and benchmark different neural network implementations independent of a concrete learning task. Our results provide insight into the capabilities and limits of neural networks and generate valuable know how for experimental design decisions.

Connected through hyperedge HE-1 (induced by source paper "rethinking generalization requires revisiting old ideas statistical mechanics approaches and complex learning behavior"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "heavy tailed universality predicts trends in test accuracies for very large pre trained deep neural networks"
    - "deep learning is robust to massive label noise"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "theory iiib generalization in deep networks"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "on the computational inefficiency of large batch sizes for stochastic gradient descent"
    - "measuring the effects of data parallelism on neural network training"

Connected through hyperedge HE-2 (induced by source paper "one bit matters understanding adversarial examples as the abuse of redundancy"):
  with members:
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "adversarial spheres"
    - "a practical approach to sizing neural networks"
    - "the helmholtz method using perceptual compression to reduce machine learning complexity"
    - "feature distillation dnn oriented jpeg compression against adversarial examples"
    - "audio adversarial examples targeted attacks on speech to text"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial attacks on neural network policies"

Connected through hyperedge HE-3 (induced by source paper "a practical approach to sizing neural networks"):
  with members:
    - "the helmholtz method using perceptual compression to reduce machine learning complexity"
    - "understanding deep learning requires rethinking generalization"
    - "deep learning and the information bottleneck principle"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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)