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: "ddrprog a clevr differentiable dynamic reasoning programmer"
  Abstract: We present a generic dynamic architecture that employs a problem specific differentiable forking mechanism to leverage discrete logical information about the problem data structure. We adapt and apply our model to CLEVR Visual Question Answering, giving rise to the DDRprog architecture; compared to previous approaches, our model achieves higher accuracy in half as many epochs with five times fewer learnable parameters. Our model directly models underlying question logic using a recurrent controller that jointly predicts and executes functional neural modules; it explicitly forks subprocesses to handle logical branching. While FiLM and other competitive models are static architectures with less supervision, we argue that inclusion of program labels enables learning of higher level logical operations -- our architecture achieves particularly high performance on questions requiring counting and integer comparison. We further demonstrate the generality of our approach though DDRstack -- an application of our method to reverse Polish notation expression evaluation in which the inclusion of a stack assumption allows our approach to generalize to long expressions, significantly outperforming an LSTM with ten times as many learnable parameters.

Connected through hyperedge HE-1 (induced by source paper "giving commands to a self driving car a multimodal reasoner for visual grounding"):
  with members:
    - "improving referring expression grounding with cross modal attention guided erasing"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "boosting entity linking performance by leveraging unlabeled documents"
    - "talk2car taking control of your self driving car"
    - "objects as points"
    - "mattnet modular attention network for referring expression comprehension"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"):
  with members:
    - "word learning and the acquisition of syntactic semantic overhypotheses"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "learning visually grounded semantics from contrastive adversarial samples"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"
    - "object level visual reasoning in videos"
    - "inferring and executing programs for visual reasoning"

Connected through hyperedge HE-4 (induced by source paper "talk2car taking control of your self driving car"):
  with members:
    - "nuscenes a multimodal dataset for autonomous driving"
    - "interactive visual grounding of referring expressions for human robot interaction"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "object referring in videos with language and human gaze"
    - "mattnet modular attention network for referring expression comprehension"
    - "deep contextualized word representations"
    - "visual referring expression recognition what do systems actually learn"

Connected through hyperedge HE-5 (induced by source paper "learning visual question answering by bootstrapping hard attention"):
  with members:
    - "hyperbolic attention networks"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "self attention with relative position representations"
    - "object based reasoning in vqa"
    - "relational inductive biases deep learning and graph networks"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"
    - "packnet adding multiple tasks to a single network by iterative pruning"

Connected through hyperedge HE-6 (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-7 (induced by source paper "neural symbolic vqa disentangling reasoning from vision and language understanding"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "explicit reasoning over end to end neural architectures for visual question answering"
    - "compositional attention networks for machine reasoning"
    - "visual question reasoning on general dependency tree"
    - "inferring and executing programs for visual reasoning"
    - "vqs linking segmentations to questions and answers for supervised attention in vqa and question focused semantic segmentation"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-8 (induced by source paper "an active information seeking model for goal oriented vision and language tasks"):
  with members:
    - "compositional attention networks for machine reasoning"
    - "inferring and executing programs for visual reasoning"
    - "improved image captioning via policy gradient optimization of spider"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "a simple neural network module for relational reasoning"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "stein variational policy gradient"
    - "are you talking to me reasoned visual dialog generation through adversarial learning"

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)