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: "in2i unsupervised multi image to image translation using generative adversarial networks"
  Abstract: In unsupervised image-to-image translation, the goal is to learn the mapping between an input image and an output image using a set of unpaired training images. In this paper, we propose an extension of the unsupervised image-to-image translation problem to multiple input setting. Given a set of paired images from multiple modalities, a transformation is learned to translate the input into a specified domain. For this purpose, we introduce a Generative Adversarial Network (GAN) based framework along with a multi-modal generator structure and a new loss term, latent consistency loss. Through various experiments we show that leveraging multiple inputs generally improves the visual quality of the translated images. Moreover, we show that the proposed method outperforms current state-of-the-art unsupervised image-to-image translation methods.

Connected through hyperedge HE-1 (induced by source paper "learning from context exploiting and interpreting file path information for better malware detection"):
  with members:
    - "aloha auxiliary loss optimization for hypothesis augmentation"
    - "ember an open dataset for training static pe malware machine learning models"
    - "meade towards a malicious email attachment detection engine"
    - "a deep learning approach to fast format agnostic detection of malicious web content"
    - "microsoft malware classification challenge"
    - "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
    - "a multi view context aware approach to android malware detection and malicious code localization"
    - "a survey of stealth malware attacks mitigation measures and steps toward autonomous open world solutions"

Connected through hyperedge HE-2 (induced by source paper "generating and aligning from data geometries with generative adversarial networks"):
  with members:
    - "magan aligning biological manifolds"
    - "geometry based data generation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "manifold valued image generation with wasserstein generative adversarial nets"
    - "cyclegan a master of steganography"
    - "generative visual manipulation on the natural image manifold"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "synthesis of high quality visible faces from polarimetric thermal faces using generative adversarial networks"):
  with members:
    - "polarimetric thermal to visible face verification via attribute preserved synthesis"
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "deep cross polarimetric thermal to visible face recognition"
    - "disentangled variational representation for heterogeneous face recognition"
    - "context encoding for semantic segmentation"
    - "densely connected pyramid dehazing network"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-4 (induced by source paper "gp gan gender preserving gan for synthesizing faces from landmarks"):
  with members:
    - "every smile is unique landmark guided diverse smile generation"
    - "face synthesis from visual attributes via sketch using conditional vaes and gans"
    - "densely connected pyramid dehazing network"
    - "began boundary equilibrium generative adversarial networks"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "pose guided person image generation"
    - "high quality facial photo sketch synthesis using multi adversarial networks"
    - "natural and effective obfuscation by head inpainting"

Connected through hyperedge HE-5 (induced by source paper "improving the performance of unimodal dynamic hand gesture recognition with multimodal training"):
  with members:
    - "deep cnn based multi task learning for open set recognition"
    - "deep transfer learning for multiple class novelty detection"
    - "deep multimodal subspace clustering networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "the kinetics human action video dataset"
    - "correlation alignment for unsupervised domain adaptation"
    - "deep coral correlation alignment for deep domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "high quality facial photo sketch synthesis using multi adversarial networks"):
  with members:
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "face synthesis from visual attributes via sketch using conditional vaes and gans"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "unsupervised creation of parameterized avatars"
    - "towards realistic face photo sketch synthesis via composition aided gans"
    - "image de raining using a conditional generative adversarial network"
    - "joint transmission map estimation and dehazing using 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)