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: "spatial knowledge distillation to aid visual reasoning"
  Abstract: For tasks involving language and vision, the current state-of-the-art methods tend not to leverage any additional information that might be present to gather relevant (commonsense) knowledge. A representative task is Visual Question Answering where large diagnostic datasets have been proposed to test a system's capability of answering questions about images. The training data is often accompanied by annotations of individual object properties and spatial locations. In this work, we take a step towards integrating this additional privileged information in the form of spatial knowledge to aid in visual reasoning. We propose a framework that combines recent advances in knowledge distillation (teacher-student framework), relational reasoning and probabilistic logical languages to incorporate such knowledge in existing neural networks for the task of Visual Question Answering. Specifically, for a question posed against an image, we use a probabilistic logical language to encode the spatial knowledge and the spatial understanding about the question in the form of a mask that is directly provided to the teacher network. The student network learns from the ground-truth information as well as the teachers prediction via distillation. We also demonstrate the impact of predicting such a mask inside the teachers network using attention. Empirically, we show that both the methods improve the test accuracy over a state-of-the-art approach on a publicly available dataset.

Connected through hyperedge HE-1 (induced by source paper "learning and reasoning for robot sequential decision making under uncertainty"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "enabling robots to understand incomplete natural language instructions using commonsense reasoning"
    - "from recognition to cognition visual commonsense reasoning"
    - "iterative visual reasoning beyond convolutions"
    - "sdrl interpretable and data efficient deep reinforcement learningleveraging symbolic planning"
    - "robot representing and reasoning with knowledge from reinforcement learning"
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "integrating human provided information into belief state representation using dynamic factorization"

Connected through hyperedge HE-2 (induced by source paper "integrating knowledge and reasoning in image understanding"):
  with members:
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "murel multimodal relational reasoning for visual question answering"
    - "deepproblog neural probabilistic logic programming"
    - "explicit reasoning over end to end neural architectures for visual question answering"
    - "a simple neural network module for relational reasoning"
    - "end to end differentiable proving"
    - "visual relationship detection with internal and external linguistic knowledge distillation"
    - "temporal relational reasoning in videos"

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)