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: "a dataset and architecture for visual reasoning with a working memory"
  Abstract: A vexing problem in artificial intelligence is reasoning about events that occur in complex, changing visual stimuli such as in video analysis or game play. Inspired by a rich tradition of visual reasoning and memory in cognitive psychology and neuroscience, we developed an artificial, configurable visual question and answer dataset (COG) to parallel experiments in humans and animals. COG is much simpler than the general problem of video analysis, yet it addresses many of the problems relating to visual and logical reasoning and memory -- problems that remain challenging for modern deep learning architectures. We additionally propose a deep learning architecture that performs competitively on other diagnostic VQA datasets (i.e. CLEVR) as well as easy settings of the COG dataset. However, several settings of COG result in datasets that are progressively more challenging to learn. After training, the network can zero-shot generalize to many new tasks. Preliminary analyses of the network architectures trained on COG demonstrate that the network accomplishes the task in a manner interpretable to humans.

Connected through hyperedge HE-1 (induced by source paper "clevrer collision events for video representation and reasoning"):
  with members:
    - "cater a diagnostic dataset for compositional actions and temporal reasoning"
    - "monet unsupervised scene decomposition and representation"
    - "cophy counterfactual learning of physical dynamics"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "heterogeneous memory enhanced multimodal attention model for video question answering"
    - "from recognition to cognition visual commonsense reasoning"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"

Connected through hyperedge HE-2 (induced by source paper "transfer learning in visual and relational reasoning"):
  with members:
    - "leveraging medical visual question answering with supporting facts"
    - "learning by abstraction the neural state machine"
    - "trends in integration of vision and language research a survey of tasks datasets and methods"
    - "huggingface s transformers state of the art natural language processing"
    - "do better imagenet models transfer better"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "taskonomy disentangling task transfer learning"
    - "object based reasoning in vqa"

Connected through hyperedge HE-3 (induced by source paper "temporal reasoning via audio question answering"):
  with members:
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "clear a dataset for compositional language and elementary acoustic reasoning"
    - "compositional attention networks for machine reasoning"
    - "tvqa localized compositional video question answering"
    - "modulating early visual processing by language"
    - "inferring and executing programs for visual reasoning"
    - "non local neural networks"

Connected through hyperedge HE-4 (induced by source paper "what is needed for simple spatial language capabilities in vqa"):
  with members:
    - "the visual qa devil in the details the impact of early fusion and batch norm on clevr"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "compositional attention networks for machine reasoning"
    - "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"
    - "shapeworld a new test methodology for multimodal language understanding"

Connected through hyperedge HE-5 (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"

Connected through hyperedge HE-6 (induced by source paper "how clever is the film model and how clever can it be"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "compositional attention networks for machine reasoning"
    - "did the model understand the question"
    - "deep reinforcement learning that matters"
    - "inferring and executing programs for visual reasoning"
    - "on the state of the art of evaluation in neural language models"
    - "a simple neural network module for relational reasoning"
    - "learning to reason end to end module networks for visual question answering"

Connected through hyperedge HE-7 (induced by source paper "cascaded mutual modulation for visual reasoning"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "compositional attention networks for machine reasoning"
    - "ddrprog a clevr differentiable dynamic reasoning programmer"
    - "object ordering with bidirectional matchings for visual reasoning"
    - "modulating early visual processing by language"
    - "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-8 (induced by source paper "the meaning of most for visual question answering models"):
  with members:
    - "learning to count objects in natural images for visual question answering"
    - "comparatives quantifiers proportions a multi task model for the learning of quantities from vision"
    - "compositional attention networks for machine reasoning"
    - "inferring and executing programs for visual reasoning"
    - "a simple neural network module for relational reasoning"
    - "shapeworld a new test methodology for multimodal language understanding"
    - "film visual reasoning with a general conditioning layer"
    - "analyzing the behavior of visual question answering models"

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.AI(Artificial Intelligence)