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: "adversarial images for variational autoencoders"
  Abstract: We investigate adversarial attacks for autoencoders. We propose a procedure that distorts the input image to mislead the autoencoder in reconstructing a completely different target image. We attack the internal latent representations, attempting to make the adversarial input produce an internal representation as similar as possible as the target's. We find that autoencoders are much more robust to the attack than classifiers: while some examples have tolerably small input distortion, and reasonable similarity to the target image, there is a quasi-linear trade-off between those aims. We report results on MNIST and SVHN datasets, and also test regular deterministic autoencoders, reaching similar conclusions in all cases. Finally, we show that the usual adversarial attack for classifiers, while being much easier, also presents a direct proportion between distortion on the input, and misdirection on the output. That proportionality however is hidden by the normalization of the output, which maps a linear layer into non-linear probabilities.

Connected through hyperedge HE-1 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection"):
  with members:
    - "curls whey boosting black box adversarial attacks"
    - "one pixel attack for fooling deep neural networks"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"
    - "minimally distorted adversarial examples with a fast adaptive boundary attack"
    - "robust adversarial perturbation on deep proposal based models"

Connected through hyperedge HE-2 (induced by source paper "disrupting deepfakes adversarial attacks against conditional image translation networks and facial manipulation systems"):
  with members:
    - "deceiving image to image translation networks for autonomous driving with adversarial perturbations"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"
    - "few shot adversarial learning of realistic neural talking head models"
    - "stargan v2 diverse image synthesis for multiple domains"
    - "fakespotter a simple yet robust baseline for spotting ai synthesized fake faces"
    - "cnn generated images are surprisingly easy to spot for now"
    - "ganimation anatomically aware facial animation from a single image"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "adversarial attack on dl based massive mimo csi feedback"):
  with members:
    - "physical adversarial attacks against end to end autoencoder communication systems"
    - "deep learning in physical layer communications"
    - "deep learning for wireless physical layer opportunities and challenges"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "deep learning for massive mimo csi feedback"
    - "convolutional radio modulation recognition networks"
    - "adversarial manipulation of deep representations"

Connected through hyperedge HE-4 (induced by source paper "interpreting adversarial examples with attributes"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "on evaluating adversarial robustness"
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "a large scale attribute dataset for zero shot learning"
    - "techniques for interpretable machine learning"

Connected through hyperedge HE-5 (induced by source paper "say what i want towards the dark side of neural dialogue models"):
  with members:
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "discrete attacks and submodular optimization with applications to text classification"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "metamorphic relation based adversarial attacks on differentiable neural computer"
    - "deep text classification can be fooled"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "neural approaches to conversational ai"
    - "detecting egregious responses in neural sequence to sequence models"

Connected through hyperedge HE-6 (induced by source paper "out domain examples for generative models"):
  with members:
    - "the gan landscape losses architectures regularization and normalization"
    - "inverting the generator of a generative adversarial network ii"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "black box adversarial attacks with limited queries and information"
    - "adversarial attacks on neural network policies"
    - "progressive growing of gans for improved quality stability and variation"
    - "precise recovery of latent vectors from generative adversarial networks"
    - "adversarial examples for semantic segmentation and object detection"

Connected through hyperedge HE-7 (induced by source paper "adversarial out domain examples for generative models"):
  with members:
    - "the gan landscape losses architectures regularization and normalization"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "black box adversarial attacks with limited queries and information"
    - "adversarial attacks on neural network policies"
    - "progressive growing of gans for improved quality stability and variation"
    - "towards the automatic anime characters creation with generative adversarial networks"
    - "precise recovery of latent vectors from generative adversarial networks"
    - "adversarial examples for semantic segmentation and object detection"

Connected through hyperedge HE-8 (induced by source paper "representation learning with autoencoders for electronic health records a comparative study"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "a survey on deep learning in medical image analysis"
    - "boosting deep learning risk prediction with generative adversarial networks for electronic health records"
    - "deepqa improving the estimation of single protein model quality with deep belief networks"
    - "safs a deep feature selection approach for precision medicine"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "representation learning a review and new perspectives"

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)