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: "counterfactual critic multi agent training for scene graph generation"
  Abstract: Scene graphs -- objects as nodes and visual relationships as edges -- describe the whereabouts and interactions of the things and stuff in an image for comprehensive scene understanding. To generate coherent scene graphs, almost all existing methods exploit the fruitful visual context by modeling message passing among objects, fitting the dynamic nature of reasoning with visual context, eg, "person" on "bike" can help to determine the relationship "ride", which in turn contributes to the category confidence of the two objects. However, we argue that the scene dynamics is not properly learned by using the prevailing cross-entropy based supervised learning paradigm, which is not sensitive to graph inconsistency: errors at the hub or non-hub nodes are unfortunately penalized equally. To this end, we propose a Counterfactual critic Multi-Agent Training (CMAT) approach to resolve the mismatch. CMAT is a multi-agent policy gradient method that frames objects as cooperative agents, and then directly maximizes a graph-level metric as the reward. In particular, to assign the reward properly to each agent, CMAT uses a counterfactual baseline that disentangles the agent-specific reward by fixing the dynamics of other agents. Extensive validations on the challenging Visual Genome benchmark show that CMAT achieves a state-of-the-art by significant performance gains under various settings and metrics.

Connected through hyperedge HE-1 (induced by source paper "unbiased scene graph generation from biased training"):
  with members:
    - "visual commonsense r cnn"
    - "deconfounded image captioning a causal retrospect"
    - "knowledge embedded routing network for scene graph generation"
    - "scene graph generation with external knowledge and image reconstruction"
    - "graphical contrastive losses for scene graph parsing"
    - "learning by abstraction the neural state machine"
    - "repair removing representation bias by dataset resampling"
    - "two causal principles for improving visual dialog"

Connected through hyperedge HE-2 (induced by source paper "counterfactual vision and language navigation via adversarial path sampling"):
  with members:
    - "adversarial active exploration for inverse dynamics model learning"
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "stay on the path instruction fidelity in vision and language navigation"
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology"
    - "transferable representation learning in vision and language navigation"
    - "the regretful agent heuristic aided navigation through progress estimation"
    - "counterfactual visual explanations"

Connected through hyperedge HE-3 (induced by source paper "auto encoding scene graphs for image captioning"):
  with members:
    - "panoptic segmentation"
    - "shuffle then assemble learning object agnostic visual relationship features"
    - "neural baby talk"
    - "learning to count objects in natural images for visual question answering"
    - "context aware visual policy network for sequence level image captioning"
    - "learning to compose dynamic tree structures for visual contexts"
    - "relational inductive biases deep learning and graph networks"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "explainable and explicit visual reasoning over scene graphs"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "learning to count objects in natural images for visual question answering"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "inferring and executing programs for visual reasoning"
    - "neural motifs scene graph parsing with global context"

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)