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: "cooperative learning with visual attributes"
  Abstract: Learning paradigms involving varying levels of supervision have received a lot of interest within the computer vision and machine learning communities. The supervisory information is typically considered to come from a human supervisor -- a "teacher" figure. In this paper, we consider an alternate source of supervision -- a "peer" -- i.e. a different machine. We introduce cooperative learning, where two agents trying to learn the same visual concepts, but in potentially different environments using different sources of data (sensors), communicate their current knowledge of these concepts to each other. Given the distinct sources of data in both agents, the mode of communication between the two agents is not obvious. We propose the use of visual attributes -- semantic mid-level visual properties such as furry, wooden, etc.-- as the mode of communication between the agents. Our experiments in three domains -- objects, scenes, and animals -- demonstrate that our proposed cooperative learning approach improves the performance of both agents as compared to their performance if they were to learn in isolation. Our approach is particularly applicable in scenarios where privacy, security and/or bandwidth constraints restrict the amount and type of information the two agents can exchange.

Connected through hyperedge HE-1 (induced by source paper "mind the gap enlarging the domain gap in open set domain adaptation"):
  with members:
    - "attract or distract exploit the margin of open set"
    - "weakly supervised open set domain adaptation by dual domain collaboration"
    - "open set domain adaptation by backpropagation"
    - "deep cocktail network multi source unsupervised domain adaptation with category shift"
    - "progressive feature alignment for unsupervised domain adaptation"
    - "image to image translation for domain adaptation"
    - "deep hashing network for unsupervised domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "multi representation knowledge distillation for audio classification"):
  with members:
    - "general audio tagging with ensembling convolutional neural networks and statistical features"
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "paraphrasing complex network network compression via factor transfer"
    - "weakly supervised crnn system for sound event detection with large scale unlabeled in domain data"
    - "large scale distributed neural network training through online distillation"
    - "sample mixed based data augmentation for domestic audio tagging"
    - "deep mutual learning"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-3 (induced by source paper "learning similarity conditions without explicit supervision"):
  with members:
    - "context aware visual compatibility prediction"
    - "learning type aware embeddings for fashion compatibility"
    - "give me a hint navigating image databases using human in the loop feedback"
    - "learning two branch neural networks for image text matching tasks"
    - "conditional image text embedding networks"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "creating capsule wardrobes from fashion images"
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"

Connected through hyperedge HE-4 (induced by source paper "scalable deep learning logo detection"):
  with members:
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "deep learning logo detection with data expansion by synthesising context"
    - "webly supervised learning of convolutional networks"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"
    - "fast r cnn"
    - "transductive zero shot action recognition by word vector embedding"

Connected through hyperedge HE-5 (induced by source paper "deep mutual learning"):
  with members:
    - "re ranking person re identification with k reciprocal encoding"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "learning deep context aware features over body and latent parts for person re identification"
    - "understanding deep learning requires rethinking generalization"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "wide residual networks"
    - "pruning filters for efficient convnets"

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)