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: "clevr ref diagnosing visual reasoning with referring expressions"
  Abstract: Referring object detection and referring image segmentation are important tasks that require joint understanding of visual information and natural language. Yet there has been evidence that current benchmark datasets suffer from bias, and current state-of-the-art models cannot be easily evaluated on their intermediate reasoning process. To address these issues and complement similar efforts in visual question answering, we build CLEVR-Ref+, a synthetic diagnostic dataset for referring expression comprehension. The precise locations and attributes of the objects are readily available, and the referring expressions are automatically associated with functional programs. The synthetic nature allows control over dataset bias (through sampling strategy), and the modular programs enable intermediate reasoning ground truth without human annotators. #R##N#In addition to evaluating several state-of-the-art models on CLEVR-Ref+, we also propose IEP-Ref, a module network approach that significantly outperforms other models on our dataset. In particular, we present two interesting and important findings using IEP-Ref: (1) the module trained to transform feature maps into segmentation masks can be attached to any intermediate module to reveal the entire reasoning process step-by-step; (2) even if all training data has at least one object referred, IEP-Ref can correctly predict no-foreground when presented with false-premise referring expressions. To the best of our knowledge, this is the first direct and quantitative proof that neural modules behave in the way they are intended.

Connected through hyperedge HE-1 (induced by source paper "cops ref a new dataset and task on compositional referring expression comprehension"):
  with members:
    - "improving referring expression grounding with cross modal attention guided erasing"
    - "visual semantic reasoning for image text matching"
    - "binary image selection bison interpretable evaluation of visual grounding"
    - "cross modal self attention network for referring image segmentation"
    - "neural sequential phrase grounding seqground"
    - "cross modal relationship inference for grounding referring expressions"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "visual coreference resolution in visual dialog using neural module networks"

Connected through hyperedge HE-2 (induced by source paper "multi task learning by a top down control network"):
  with members:
    - "panoptic segmentation"
    - "superposition of many models into one"
    - "many task learning with task routing"
    - "attentive single tasking of multiple tasks"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "top down feedback for crowd counting convolutional neural network"
    - "multi task learning as multi objective optimization"
    - "progressive neural networks"

Connected through hyperedge HE-3 (induced by source paper "robust change captioning"):
  with members:
    - "clevr dialog a diagnostic dataset for multi round reasoning in visual dialog"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural baby talk"
    - "discriminability objective for training descriptive captions"
    - "learning to describe differences between pairs of similar images"
    - "explainable neural computation via stack neural module networks"

Connected through hyperedge HE-4 (induced by source paper "language conditioned graph networks for relational reasoning"):
  with members:
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "murel multimodal relational reasoning for visual question answering"
    - "graph neural networks a review of methods and applications"
    - "iterative visual reasoning beyond convolutions"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "classifying collisions with spatio temporal action graph networks"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "mattnet modular attention network for referring expression comprehension"

Connected through hyperedge HE-5 (induced by source paper "dynamic graph attention for referring expression comprehension"):
  with members:
    - "cross modal relationship inference for grounding referring expressions"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "mattnet modular attention network for referring expression comprehension"
    - "compositional attention networks for machine reasoning"
    - "visual question reasoning on general dependency tree"
    - "inferring and executing programs for visual reasoning"
    - "a simple neural network module for relational reasoning"
    - "learning to reason end to end module networks for visual question answering"

Connected through hyperedge HE-6 (induced by source paper "clevr dialog a diagnostic dataset for multi round reasoning in visual dialog"):
  with members:
    - "referring relationships"
    - "end to end neural coreference resolution"
    - "generating descriptions with grounded and co referenced people"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "learning to reason end to end module networks for visual question answering"
    - "improving coreference resolution by learning entity level distributed representations"
    - "learning global features for coreference resolution"
    - "guesswhat visual object discovery through multi modal dialogue"

Connected through hyperedge HE-7 (induced by source paper "a spoken dialogue system for spatial question answering in a physical blocks world"):
  with members:
    - "clevr dialog a diagnostic dataset for multi round reasoning in visual dialog"
    - "a type coherent expressive representation as an initial step to language understanding"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "acquiring common sense spatial knowledge through implicit spatial templates"
    - "learning interpretable spatial operations in a rich 3d blocks world"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"

Connected through hyperedge HE-8 (induced by source paper "deep nets what have they ever done for vision"):
  with members:
    - "the elephant in the room"
    - "deep learning a critical appraisal"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "progressive neural architecture search"
    - "few shot image recognition by predicting parameters from activations"
    - "unsupervised learning of depth and ego motion from video"
    - "transfer of view manifold learning to similarity perception of novel objects"

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)