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: "25 years of cnns can we compare to human abstraction capabilities"
  Abstract: We try to determine the progress made by convolutional neural networks over the past 25 years in classifying images into abstractc lasses. For this purpose we compare the performance of LeNet to that of GoogLeNet at classifying randomly generated images which are differentiated by an abstract property (e.g., one class contains two objects of the same size, the other class two objects of different sizes). Our results show that there is still work to do in order to solve vision problems humans are able to solve without much difficulty.

Connected through hyperedge HE-1 (induced by source paper "evaluating the progress of deep learning for visual relational concepts"):
  with members:
    - "v prom a benchmark for visual reasoning using visual progressive matrices"
    - "same different problems strain convolutional neural networks"
    - "a simple neural network module for relational reasoning"
    - "squeeze and excitation networks"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "iq of neural networks"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "a study and comparison of human and deep learning recognition performance under visual distortions"

Connected through hyperedge HE-2 (induced by source paper "v prom a benchmark for visual reasoning using visual progressive matrices"):
  with members:
    - "visual question answering as a meta learning task"
    - "one shot visual imitation learning via meta learning"
    - "learning generalized reactive policies using deep neural networks"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "a simple neural network module for relational reasoning"
    - "an analysis of visual question answering algorithms"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "learning robust rewards with adversarial inverse reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "few shot abstract visual reasoning with spectral features"):
  with members:
    - "compositional attention networks for machine reasoning"
    - "not so clevr visual relations strain feedforward neural networks"
    - "a simple neural network module for relational reasoning"
    - "prototypical networks for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"
    - "going deeper with convolutions"

Connected through hyperedge HE-4 (induced by source paper "toward automatic comparison of visualization techniques application to graph visualization"):
  with members:
    - "evaluating the readability of force directed graph layouts a deep learning approach"
    - "bigdl a distributed deep learning framework for big data"
    - "can the early human visual system compete with deep neural networks"
    - "a study and comparison of human and deep learning recognition performance under visual distortions"
    - "xception deep learning with depthwise separable convolutions"
    - "learning to count with deep object features"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "recognizing manipulation actions from state transformations"):
  with members:
    - "vrkitchen an interactive 3d virtual environment for task oriented learning"
    - "object level visual reasoning in videos"
    - "joint discovery of object states and manipulation actions"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "temporal segment networks towards good practices for deep action recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-6 (induced by source paper "program synthesis performance constrained by non linear spatial relations in synthetic visual reasoning test"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "gamepad a learning environment for theorem proving"
    - "towards neural theorem proving at scale"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "evaluating cnns on the gestalt principle of closure"):
  with members:
    - "do neural networks show gestalt phenomena an exploration of the law of closure"
    - "going deeper with convolutions"

Connected through hyperedge HE-8 (induced by source paper "object level visual reasoning in videos"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "scaling egocentric vision the epic kitchens dataset"
    - "moments in time dataset one million videos for event understanding"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "compositional attention networks for machine reasoning"
    - "not so clevr visual relations strain feedforward neural networks"
    - "predicting deeper into the future of semantic segmentation"

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.CV(Computer Vision and Pattern Recognition)