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: "the hasyv2 dataset"
  Abstract: This paper describes the HASYv2 dataset. HASY is a publicly available, free of charge dataset of single symbols similar to MNIST. It contains 168233 instances of 369 classes. HASY contains two challenges: A classification challenge with 10 pre-defined folds for 10-fold cross-validation and a verification challenge.

Connected through hyperedge HE-1 (induced by source paper "keyhole imaging non line of sight imaging and tracking of moving objects along a single optical path at long standoff distances"):
  with members:
    - "fast back projection for non line of sight reconstruction"
    - "optically lightweight tracking of objects around a corner"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "understanding convolutional neural networks with information theory an initial exploration"):
  with members:
    - "adaptive estimators show information compression in deep neural networks"
    - "understanding individual neuron importance using information theory"
    - "understanding autoencoders with information theoretic concepts"
    - "estimating information flow in neural networks"
    - "simple stopping criteria for information theoretic feature selection"
    - "emergence of invariance and disentanglement in deep representations"
    - "thinet a filter level pruning method for deep neural network compression"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-3 (induced by source paper "tunneling neural perception and logic reasoning through abductive learning"):
  with members:
    - "a simple neural network module for relational reasoning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "adversarial examples for evaluating reading comprehension systems"
    - "harnessing deep neural networks with logic rules"

Connected through hyperedge HE-4 (induced by source paper "generative openmax for multi class open set classification"):
  with members:
    - "mask r cnn"
    - "unsupervised learning for physical interaction through video prediction"
    - "invertible conditional gans for image editing"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "improved techniques for training gans"
    - "image to image translation with conditional adversarial networks"
    - "towards open set deep 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.CV(Computer Vision and Pattern Recognition)