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: "cloudcv large scale distributed computer vision as a cloud service"
  Abstract: We are witnessing a proliferation of massive visual data. Unfortunately scaling existing computer vision algorithms to large datasets leaves researchers repeatedly solving the same algorithmic, logistical, and infrastructural problems. Our goal is to democratize computer vision; one should not have to be a computer vision, big data and distributed computing expert to have access to state-of-the-art distributed computer vision algorithms. We present CloudCV, a comprehensive system to provide access to state-of-the-art distributed computer vision algorithms as a cloud service through a Web Interface and APIs.

Connected through hyperedge HE-1 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization"):
  with members:
    - "taking a hint leveraging explanations to make vision and language models more grounded"
    - "choose your neuron incorporating domain knowledge through neuron importance"
    - "network dissection quantifying interpretability of deep visual representations"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "iqa visual question answering in interactive environments"
    - "embodied question answering"
    - "analyzing the behavior of visual question answering models"

Connected through hyperedge HE-2 (induced by source paper "adacompress adaptive compression for online computer vision services"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deepn jpeg a deep neural network favorable jpeg based image compression framework"
    - "jalad joint accuracy and latency aware deep structure decoupling for edge cloud execution"
    - "towards image understanding from deep compression without decoding"
    - "robust physical world attacks on deep learning models"
    - "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning"
    - "full resolution image compression with recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "the extreme value machine"):
  with members:
    - "sparse representation based open set recognition"
    - "towards open world recognition"
    - "sequential score adaptation with extreme value theory for robust railway track inspection"
    - "imagenet large scale visual recognition challenge"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "deep learning using linear support vector machines"

Connected through hyperedge HE-4 (induced by source paper "towards transparent ai systems interpreting visual question answering models"):
  with members:
    - "why should i trust you explaining the predictions of any classifier"
    - "exploring models and data for image question answering"
    - "vqa visual question answering"
    - "ask your neurons a neural based approach to answering questions about images"
    - "large scale simple question answering with memory networks"
    - "striving for simplicity the all convolutional net"
    - "sequence to sequence learning with neural networks"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "radiation search operations using scene understanding with autonomous uav and ugv"):
  with members:
    - "orb slam a versatile and accurate monocular slam system"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"

Connected through hyperedge HE-6 (induced by source paper "vqa visual question answering"):
  with members:
    - "exploring models and data for image question answering"
    - "don t just listen use your imagination leveraging visual common sense for non visual tasks"
    - "skip thought vectors"
    - "microsoft coco captions data collection and evaluation server"
    - "are you talking to a machine dataset and methods for multilingual image question answering"
    - "yin and yang balancing and answering binary visual questions"
    - "very deep convolutional networks for large scale image recognition"
    - "unifying visual semantic embeddings with multimodal neural language models"

Connected through hyperedge HE-7 (induced by source paper "understanding image virality"):
  with members:
    - "becoming the expert interactive multi class machine teaching"
    - "deep feelings a massive cross lingual study on the relation between emotions and virality"
    - "deep fragment embeddings for bidirectional image sentence mapping"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "exploring image virality in google plus"
    - "a scalable heuristic for viral marketing under the tipping model"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "towards open world recognition"):
  with members:
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "overfeat integrated recognition localization and detection using convolutional 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)