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: "cross modal subspace learning for fine grained sketch based image retrieval"
  Abstract: Sketch-based image retrieval (SBIR) is challenging due to the inherent domain-gap between sketch and photo. Compared with pixel-perfect depictions of photos, sketches are iconic renderings of the real world with highly abstract. Therefore, matching sketch and photo directly using low-level visual clues are unsufficient, since a common low-level subspace that traverses semantically across the two modalities is non-trivial to establish. Most existing SBIR studies do not directly tackle this cross-modal problem. This naturally motivates us to explore the effectiveness of cross-modal retrieval methods in SBIR, which have been applied in the image-text matching successfully. In this paper, we introduce and compare a series of state-of-the-art cross-modal subspace learning methods and benchmark them on two recently released fine-grained SBIR datasets. Through thorough examination of the experimental results, we have demonstrated that the subspace learning can effectively model the sketch-photo domain-gap. In addition we draw a few key insights to drive future research.

Connected through hyperedge HE-1 (induced by source paper "deep self supervised representation learning for free hand sketch"):
  with members:
    - "deep learning for free hand sketch a survey"
    - "zero reference deep curve estimation for low light image enhancement"
    - "generative adversarial zero shot relational learning for knowledge graphs"
    - "the devil is in the channels mutual channel loss for fine grained image classification"
    - "multi graph transformer for free hand sketch recognition"
    - "learning for multi model and multi type fitting"
    - "doodle to search practical zero shot sketch based image retrieval"
    - "revisiting self supervised visual representation learning"

Connected through hyperedge HE-2 (induced by source paper "fine grained instance level sketch based video retrieval"):
  with members:
    - "deep learning for free hand sketch a survey"
    - "deep self supervised representation learning for free hand sketch"
    - "livesketch query perturbations for guided sketch based visual search"
    - "deep zero shot learning for scene sketch"
    - "learning deep sketch abstraction"
    - "self supervised video hashing with hierarchical binary auto encoder"
    - "multi fiber networks for video recognition"
    - "sketchmate deep hashing for million scale human sketch retrieval"

Connected through hyperedge HE-3 (induced by source paper "channel attention with embedding gaussian process a probabilistic methodology"):
  with members:
    - "pay attention to the activations a modular attention mechanism for fine grained image recognition"
    - "stochastic region pooling make attention more expressive"
    - "selective kernel networks"
    - "dianet dense and implicit attention network"
    - "bam bottleneck attention module"
    - "multi attention multi class constraint for fine grained image recognition"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "gather excite exploiting feature context in convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "deep zero shot learning for scene sketch"):
  with members:
    - "learning deep sketch abstraction"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "a zero shot framework for sketch based image retrieval"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "generalized zero shot recognition based on visually semantic embedding"
    - "sketchyscene richly annotated scene sketches"
    - "unsupervised person re identification by deep learning tracklet association"
    - "semantic autoencoder for zero shot learning"

Connected through hyperedge HE-5 (induced by source paper "channel max pooling layer for fine grained vehicle classification"):
  with members:
    - "decorrelation of neutral vector variables theory and applications"
    - "the role of data analysis in the development of intelligent energy networks"
    - "low rank bilinear pooling for fine grained classification"
    - "sgdr stochastic gradient descent with restarts"
    - "learning a discriminative filter bank within a cnn for fine grained recognition"
    - "a large scale car dataset for fine grained categorization and verification"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"

Connected through hyperedge HE-6 (induced by source paper "recsys dan discriminative adversarial networks for cross domain recommender systems"):
  with members:
    - "lrmm learning to recommend with missing modalities"
    - "decorrelation of neutral vector variables theory and applications"
    - "neural collaborative filtering"
    - "adversarial discriminative domain adaptation"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "learning to sketch with shortcut cycle consistency"):
  with members:
    - "sketch pix2seq a model to generate sketches of multiple categories"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "fader networks manipulating images by sliding attributes"
    - "a neural representation of sketch drawings"
    - "dualgan unsupervised dual learning for image to image translation"
    - "instance normalization the missing ingredient for fast stylization"
    - "convolutional sketch inversion"

Connected through hyperedge HE-8 (induced by source paper "sketch a classifier sketch based photo classifier generation"):
  with members:
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "learning to learn by gradient descent by gradient descent"
    - "learning a deep embedding model for zero shot learning"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "synthesized classifiers for zero shot learning"
    - "diet networks thin parameters for fat genomic"
    - "semi supervised vocabulary informed learning"
    - "low shot visual object recognition"

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)