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 attentional context learning for rgb d object detection"
  Abstract: Recognizing objects from simultaneously sensed photometric (RGB) and depth channels is a fundamental yet practical problem in many machine vision applications, such as robot grasping and autonomous driving. In this paper, we address this problem by developing a cross-modal attentional context (CMAC) learning framework, which enables the full exploitation of the context information from both RGB and depth data. Compared to existing RGB-D object detection frameworks, our approach has several appealing properties. First, it consists of an attention-based global context model for exploiting adaptive contextual information and incorporating this information into a region-based CNN (e.g., fast RCNN) framework to achieve improved object detection performance. Second, our CMAC framework further contains a fine-grained object part attention module to harness multiple discriminative object parts inside each possible object region for superior local feature representation. While greatly improving the accuracy of RGB-D object detection, the effective cross-modal information fusion as well as attentional context modeling in our proposed model provide an interpretable visualization scheme. Experimental results demonstrate that the proposed method significantly improves upon the state of the art on all public benchmarks.

Connected through hyperedge HE-1 (induced by source paper "relationship embedded representation learning for grounding referring expressions"):
  with members:
    - "cross modal relationship inference for grounding referring expressions"
    - "murel multimodal relational reasoning for visual question answering"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "interpretable visual question answering by visual grounding from attention supervision mining"
    - "a neural compositional paradigm for image captioning"
    - "graph r cnn for scene graph generation"
    - "contrast oriented deep neural networks for salient object detection"
    - "mattnet modular attention network for referring expression comprehension"

Connected through hyperedge HE-2 (induced by source paper "cross modal relationship inference for grounding referring expressions"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "contrast oriented deep neural networks for salient object detection"
    - "mattnet modular attention network for referring expression comprehension"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "exploring visual relationship for image captioning"
    - "context encoding for semantic segmentation"
    - "grounding referring expressions in images by variational context"
    - "neural motifs scene graph parsing with global context"

Connected through hyperedge HE-3 (induced by source paper "what can computational models learn from human selective attention a review from an audiovisual crossmodal perspective"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "a self attention based deep learning method for lesion attribute detection from ct reports"
    - "enhanced robot speech recognition using biomimetic binaural sound source localization"
    - "representation similarity analysis for efficient task taxonomy transfer learning"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "a neurorobotic experiment for crossmodal conflict resolution in complex environments"
    - "end to end saliency mapping via probability distribution prediction"
    - "assessing the contribution of semantic congruency to multisensory integration and conflict resolution"

Connected through hyperedge HE-4 (induced by source paper "face hallucination by attentive sequence optimization with reinforcement learning"):
  with members:
    - "facial landmark machines a backbone branches architecture with progressive representation learning"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "attention aware face hallucination via deep reinforcement learning"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "zero shot super resolution using deep internal learning"
    - "parallel multiscale autoregressive density estimation"
    - "learning to hallucinate face images via component generation and enhancement"

Connected through hyperedge HE-5 (induced by source paper "difficulty aware image super resolution via deep adaptive dual network"):
  with members:
    - "pirm challenge on perceptual image enhancement on smartphones report"
    - "fast and accurate single image super resolution via information distillation network"
    - "image super resolution using very deep residual channel attention networks"
    - "multi level wavelet cnn for image restoration"
    - "attention aware face hallucination via deep reinforcement learning"
    - "structure preserving image super resolution via contextualized multitask learning"
    - "enhanced deep residual networks for single image super resolution"
    - "memnet a persistent memory network for image restoration"

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)