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: "neural task planning with and or graph representations"
  Abstract: This paper focuses on semantic task planning, i.e., predicting a sequence of actions toward accomplishing a specific task under a certain scene, which is a new problem in computer vision research. The primary challenges are how to model task-specific knowledge and how to integrate this knowledge into the learning procedure. In this work, we propose training a recurrent long short-term memory (LSTM) network to address this problem, i.e., taking a scene image (including pre-located objects) and the specified task as input and recurrently predicting action sequences. However, training such a network generally requires large numbers of annotated samples to cover the semantic space (e.g., diverse action decomposition and ordering). To overcome this issue, we introduce a knowledge and-or graph (AOG) for task description, which hierarchically represents a task as atomic actions. With this AOG representation, we can produce many valid samples (i.e., action sequences according to common sense) by training another auxiliary LSTM network with a small set of annotated samples. Furthermore, these generated samples (i.e., task-oriented action sequences) effectively facilitate training of the model for semantic task planning. In our experiments, we create a new dataset that contains diverse daily tasks and extensively evaluate the effectiveness of our approach.

Connected through hyperedge HE-1 (induced by source paper "knowledge graph transfer network for few shot recognition"):
  with members:
    - "knowledge embedded routing network for scene graph generation"
    - "variational prototyping encoder one shot learning with prototypical images"
    - "learning semantic specific graph representation for multi label image recognition"
    - "knowledge embedded representation learning for fine grained image recognition"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "hybrid knowledge routed modules for large scale object detection"
    - "visual semantic navigation using scene priors"

Connected through hyperedge HE-2 (induced by source paper "knowledge embedded routing network for scene graph generation"):
  with members:
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "neural motifs scene graph parsing with global context"
    - "multi label zero shot learning with structured knowledge graphs"
    - "detecting visual relationships with deep relational networks"
    - "scene graph generation by iterative message passing"
    - "learning to detect human object interactions"
    - "knowledge guided recurrent neural network learning for task oriented action prediction"
    - "scene graph generation from objects phrases and region captions"

Connected through hyperedge HE-3 (induced by source paper "aognets compositional grammatical architectures for deep learning"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "taking human out of learning applications a survey on automated machine learning"
    - "cbam convolutional block attention module"
    - "squeezenext hardware aware neural network design"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "network dissection quantifying interpretability of deep visual representations"
    - "learning transferable architectures for scalable image recognition"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning 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.CV(Computer Vision and Pattern Recognition)