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: "knowledge guided recurrent neural network learning for task oriented action prediction"
  Abstract: This paper aims at task-oriented action prediction, i.e., predicting a sequence of actions towards accomplishing a specific task under a certain scene, which is a new problem in computer vision research. The main challenges lie in how to model task-specific knowledge and integrate it in the learning procedure. In this work, we propose to train a recurrent long-short term memory (LSTM) network for handling 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 usually requires large amounts of annotated samples for covering the semantic space (e.g., diverse action decomposition and ordering). To alleviate this issue, we introduce a temporal And-Or graph (AOG) for task description, which hierarchically represents a task into atomic actions. With this AOG representation, we can produce many valid samples (i.e., action sequences according with common sense) by training another auxiliary LSTM network with a small set of annotated samples. And these generated samples (i.e., task-oriented action sequences) effectively facilitate training the model for task-oriented action prediction. In the experiments, we create a new dataset containing diverse daily tasks and extensively evaluate the effectiveness of our approach.

Connected through hyperedge HE-1 (induced by source paper "knowledge embedded routing network for scene graph generation"):
  with members:
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "neural task planning with and or graph representations"
    - "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"
    - "scene graph generation from objects phrases and region captions"

Connected through hyperedge HE-2 (induced by source paper "learning deep representations for semantic image parsing a comprehensive overview"):
  with members:
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "progressively diffused networks for semantic image segmentation"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "interpretable structure evolving lstm"
    - "label refinement network for coarse to fine semantic segmentation"
    - "instance level salient object segmentation"
    - "semantic instance segmentation via deep metric learning"

Connected through hyperedge HE-3 (induced by source paper "neural task planning with and or graph representations"):
  with members:
    - "fine grained representation learning and recognition by exploiting hierarchical semantic embedding"
    - "learning model based planning from scratch"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "diversified visual attention networks for fine grained object classification"
    - "visual semantic planning using deep successor representations"
    - "cognitive mapping and planning for visual navigation"
    - "ai2 thor an interactive 3d environment for visual ai"

Connected through hyperedge HE-4 (induced by source paper "deep reasoning with knowledge graph for social relationship understanding"):
  with members:
    - "a domain based approach to social relation recognition"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "dual glance model for deciphering social relationships"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "hadamard product for low rank bilinear pooling"
    - "semantic object parsing with graph lstm"
    - "gated graph sequence neural networks"
    - "graph structured representations for visual question answering"

Connected through hyperedge HE-5 (induced by source paper "knowledge embedded representation learning for fine grained image recognition"):
  with members:
    - "crowd counting using deep recurrent spatial aware network"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "semantic object parsing with graph lstm"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"
    - "gated graph sequence neural networks"
    - "learning convolutional neural networks for graphs"
    - "compact bilinear pooling"

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)