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: "tallyqa answering complex counting questions"
  Abstract: Most counting questions in visual question answering (VQA) datasets are simple and require no more than object detection. Here, we study algorithms for complex counting questions that involve relationships between objects, attribute identification, reasoning, and more. To do this, we created TallyQA, the world's largest dataset for open-ended counting. We propose a new algorithm for counting that uses relation networks with region proposals. Our method lets relation networks be efficiently used with high-resolution imagery. It yields state-of-the-art results compared to baseline and recent systems on both TallyQA and the HowMany-QA benchmark.

Connected through hyperedge HE-1 (induced by source paper "answer them all toward universal visual question answering models"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "vqd visual query detection in natural scenes"
    - "the visual qa devil in the details the impact of early fusion and batch norm on clevr"
    - "bilinear attention networks"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "reciprocal attention fusion for visual question answering"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "learning visual question answering by bootstrapping hard attention"

Connected through hyperedge HE-2 (induced by source paper "scene text visual question answering"):
  with members:
    - "towards vqa models that can read"
    - "icdar 2019 competition on scene text visual question answering"
    - "single shot scene text retrieval"
    - "fots fast oriented text spotting with a unified network"
    - "an end to end textspotter with explicit alignment and attention"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "textboxes a single shot oriented scene text detector"
    - "rosetta large scale system for text detection and recognition in images"

Connected through hyperedge HE-3 (induced by source paper "from two graphs to n questions a vqa dataset for compositional reasoning on vision and commonsense"):
  with members:
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "from recognition to cognition visual commonsense reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "relational inductive biases deep learning and graph networks"
    - "overcoming language priors in visual question answering with adversarial regularization"
    - "vip cnn visual phrase guided convolutional neural network"
    - "inferring and executing programs for visual reasoning"
    - "mutan multimodal tucker fusion for visual question answering"

Connected through hyperedge HE-4 (induced by source paper "answering questions about data visualizations using efficient bimodal fusion"):
  with members:
    - "towards vqa models that can read"
    - "answer them all toward universal visual question answering models"
    - "the visual qa devil in the details the impact of early fusion and batch norm on clevr"
    - "bilinear attention networks"
    - "figurenet a deep learning model for question answering on scientific plots"
    - "dvqa understanding data visualizations via question answering"
    - "scatteract automated extraction of data from scatter plots"
    - "inferring and executing programs for visual reasoning"

Connected through hyperedge HE-5 (induced by source paper "visual question answering using deep learning a survey and performance analysis"):
  with members:
    - "differential attention for visual question answering"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "an analysis of visual question answering algorithms"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "memexqa visual memex question answering"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "visual7w grounded question answering in images"
    - "exploring models and data for image question answering"

Connected through hyperedge HE-6 (induced by source paper "vqd visual query detection in natural scenes"):
  with members:
    - "panoptic segmentation"
    - "answer them all toward universal visual question answering models"
    - "dvqa understanding data visualizations via question answering"
    - "an analysis of visual question answering algorithms"
    - "a joint speaker listener reinforcer model for referring expressions"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "combining multiple cues for visual madlibs question answering"
    - "making the v in vqa matter elevating the role of image understanding in visual question answering"

Connected through hyperedge HE-7 (induced by source paper "multimodal logical inference system for visual textual entailment"):
  with members:
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "visual entailment task for visually grounded language learning"
    - "comparatives quantifiers proportions a multi task model for the learning of quantities from vision"
    - "grounded textual entailment"
    - "attentive relational networks for mapping images to scene graphs"
    - "scene graph generation by iterative message passing"
    - "graph structured representations for visual question answering"
    - "vqa visual question answering"

Connected through hyperedge HE-8 (induced by source paper "a corpus for reasoning about natural language grounded in photographs"):
  with members:
    - "binary image selection bison interpretable evaluation of visual grounding"
    - "visual entailment a novel task for fine grained image understanding"
    - "from recognition to cognition visual commonsense reasoning"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "using syntax to ground referring expressions in natural images"
    - "learning visual question answering by bootstrapping hard attention"
    - "talk the walk navigating new york city through grounded dialogue"

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)