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: "information in infinite ensembles of infinitely wide neural networks"
  Abstract: In this preliminary work, we study the generalization properties of infinite ensembles of infinitely-wide neural networks. Amazingly, this model family admits tractable calculations for many information-theoretic quantities. We report analytical and empirical investigations in the search for signals that correlate with generalization.

Connected through hyperedge HE-1 (induced by source paper "forgetting outside the box scrubbing deep networks of information accessible from input output observations"):
  with members:
    - "machine unlearning linear filtration for logit based classifiers"
    - "thieves on sesame street model extraction of bert based apis"
    - "where is the information in a deep neural network"
    - "eternal sunshine of the spotless net selective forgetting in deep networks"
    - "enhanced convolutional neural tangent kernels"
    - "on exact computation with an infinitely wide neural net"
    - "certified data removal from machine learning models"
    - "machine unlearning"

Connected through hyperedge HE-2 (induced by source paper "on the infinite width limit of neural networks with a standard parameterization"):
  with members:
    - "freeze and chaos for dnns an ntk view of batch normalization checkerboard and boundary effects"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "asymptotics of wide networks from feynman diagrams"
    - "harnessing the power of infinitely wide deep nets on small data tasks"
    - "the effect of network width on stochastic gradient descent and generalization an empirical study"
    - "on exact computation with an infinitely wide neural net"
    - "graph neural tangent kernel fusing graph neural networks with graph kernels"
    - "neural tangent kernel convergence and generalization in 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)