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: "topoact exploring the shape of activations in deep learning"
  Abstract: Deep neural networks such as GoogLeNet and ResNet have achieved superhuman performance in tasks like image classification. To understand how such superior performance is achieved, we can probe a trained deep neural network by studying neuron activations, that is, combinations of neuron firings, at any layer of the network in response to a particular input. With a large set of input images, we aim to obtain a global view of what neurons detect by studying their activations. We ask the following questions: What is the shape of the space of activations? That is, what is the organizational principle behind neuron activations, and how are the activations related within a layer and across layers? Applying tools from topological data analysis, we present TopoAct, a visual exploration system used to study topological summaries of activation vectors for a single layer as well as the evolution of such summaries across multiple layers. We present visual exploration scenarios using TopoAct that provide valuable insights towards learned representations of an image classifier.

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.CG(Computational Geometry)