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: "object oriented dynamics predictor"
  Abstract: Generalization has been one of the major challenges for learning dynamics models in model-based reinforcement learning. However, previous work on action-conditioned dynamics prediction focuses on learning the pixel-level motion and thus does not generalize well to novel environments with different object layouts. In this paper, we present a novel object-oriented framework, called object-oriented dynamics predictor (OODP), which decomposes the environment into objects and predicts the dynamics of objects conditioned on both actions and object-to-object relations. It is an end-to-end neural network and can be trained in an unsupervised manner. To enable the generalization ability of dynamics learning, we design a novel CNN-based relation mechanism that is class-specific (rather than object-specific) and exploits the locality principle. Empirical results show that OODP significantly outperforms previous methods in terms of generalization over novel environments with various object layouts. OODP is able to learn from very few environments and accurately predict dynamics in a large number of unseen environments. In addition, OODP learns semantically and visually interpretable dynamics models.

Connected through hyperedge HE-1 (induced by source paper "transformation based adversarial video prediction on large scale data"):
  with members:
    - "a short note on the kinetics 700 human action dataset"
    - "large scale adversarial representation learning"
    - "high fidelity speech synthesis with adversarial networks"
    - "adversarial video generation on complex datasets"
    - "scaling autoregressive video models"
    - "tganv2 efficient training of large models for video generation with multiple subsampling layers"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "exploration by random network distillation"

Connected through hyperedge HE-2 (induced by source paper "unsupervised state representation learning in atari"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "on variational bounds of mutual information"
    - "multi object representation learning with iterative variational inference"
    - "wasserstein dependency measure for representation learning"
    - "learning representations by maximizing mutual information across views"
    - "revisiting self supervised visual representation learning"
    - "noise contrastive estimation and negative sampling for conditional models consistency and statistical efficiency"
    - "near optimal representation learning for hierarchical reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "scoring aggregating planning learning task agnostic priors from interactions and sparse rewards for zero shot generalization"):
  with members:
    - "learning to generalize from sparse and underspecified rewards"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "model based reinforcement learning for atari"
    - "mapping state space using landmarks for universal goal reaching"
    - "inverse reinforcement learning for video games"
    - "grasp2vec learning object representations from self supervised grasping"
    - "count based exploration with the successor representation"
    - "exploration by random network distillation"

Connected through hyperedge HE-4 (induced by source paper "object oriented dynamics learning through multi level abstraction"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "relational deep reinforcement learning"
    - "deep learning for generic object detection a survey"
    - "relational inductive biases deep learning and graph networks"
    - "gotta learn fast a new benchmark for generalization in rl"
    - "contingency aware exploration in reinforcement learning"
    - "visual interaction networks"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-5 (induced by source paper "physics as inverse graphics joint unsupervised learning of objects and physics from video"):
  with members:
    - "unsupervised discovery of parts structure and dynamics"
    - "learning programmatically structured representations with perceptor gradients"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "learning latent dynamics for planning from pixels"
    - "unsupervised learning of latent physical properties using perception prediction networks"

Connected through hyperedge HE-6 (induced by source paper "joint learning of unsupervised object based perception and control"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "unsupervised learning of object keypoints for perception and control"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "multi object representation learning with iterative variational inference"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "strategic object oriented reinforcement learning"
    - "relational deep reinforcement learning"
    - "unsupervised video object segmentation for deep reinforcement 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)