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: "a joint model of language and perception for grounded attribute learning"
  Abstract: As robots become more ubiquitous and capable, it becomes ever more important to enable untrained users to easily interact with them. Recently, this has led to study of the language grounding problem, where the goal is to extract representations of the meanings of natural language tied to perception and actuation in the physical world. In this paper, we present an approach for joint learning of language and perception models for grounded attribute induction. Our perception model includes attribute classifiers, for example to detect object color and shape, and the language model is based on a probabilistic categorial grammar that enables the construction of rich, compositional meaning representations. The approach is evaluated on the task of interpreting sentences that describe sets of objects in a physical workspace. We demonstrate accurate task performance and effective latent-variable concept induction in physical grounded scenes.

Connected through hyperedge HE-1 (induced by source paper "cops ref a new dataset and task on compositional referring expression comprehension"):
  with members:
    - "improving referring expression grounding with cross modal attention guided erasing"
    - "visual semantic reasoning for image text matching"
    - "clevr ref diagnosing visual reasoning with referring expressions"
    - "binary image selection bison interpretable evaluation of visual grounding"
    - "cross modal self attention network for referring image segmentation"
    - "neural sequential phrase grounding seqground"
    - "cross modal relationship inference for grounding referring expressions"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"

Connected through hyperedge HE-2 (induced by source paper "reward rational implicit choice a unifying formalism for reward learning"):
  with members:
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "preferences implicit in the state of the world"
    - "active inverse reward design"
    - "inverse reward design"
    - "learning preferences for manipulation tasks from online coactive feedback"
    - "generative adversarial imitation learning"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "the off switch game"

Connected through hyperedge HE-3 (induced by source paper "learning to follow directions in street view"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning visual question answering by bootstrapping hard attention"
    - "global pose estimation with an attention based recurrent network"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "gibson env real world perception for embodied agents"
    - "chalet cornell house agent learning environment"

Connected through hyperedge HE-4 (induced by source paper "deep bayesian active learning for multiple correct outputs"):
  with members:
    - "why does a visual question have different answers"
    - "bertscore evaluating text generation with bert"
    - "confidence modeling for neural semantic parsing"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "mutan multimodal tucker fusion for visual question answering"
    - "deep active learning for named entity recognition"
    - "learning by asking questions"
    - "active learning for visual question answering an empirical study"

Connected through hyperedge HE-5 (induced by source paper "weakly supervised spatio temporally grounding natural sentence in video"):
  with members:
    - "using syntax to ground referring expressions in natural images"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "motion appearance co memory networks for video question answering"
    - "object referring in videos with language and human gaze"
    - "real time referring expression comprehension by single stage grounding network"
    - "mattnet modular attention network for referring expression comprehension"
    - "knowledge aided consistency for weakly supervised phrase grounding"
    - "weakly supervised video object grounding from text by loss weighting and object interaction"

Connected through hyperedge HE-6 (induced by source paper "learning to map natural language instructions to physical quadcopter control using simulated flight"):
  with members:
    - "stay on the path instruction fidelity in vision and language navigation"
    - "executing instructions in situated collaborative interactions"
    - "chasing ghosts instruction following as bayesian state tracking"
    - "universal planning networks"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "speaker follower models for vision and language navigation"
    - "situated mapping of sequential instructions to actions with single step reward observation"
    - "mapping instructions to actions in 3d environments with visual goal prediction"

Connected through hyperedge HE-7 (induced by source paper "improving grounded natural language understanding through human robot dialog"):
  with members:
    - "teaching robots to imitate a human with no on teacher sensors what are the key challenges"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"
    - "follownet robot navigation by following natural language directions with deep reinforcement learning"
    - "visual curiosity learning to ask questions to learn visual recognition"
    - "neural amr sequence to sequence models for parsing and generation"
    - "learning interpretable spatial operations in a rich 3d blocks world"
    - "vision and language navigation interpreting visually grounded navigation instructions in real environments"
    - "language to logical form with neural attention"

Connected through hyperedge HE-8 (induced by source paper "robot supervised learning for object segmentation"):
  with members:
    - "lvis a dataset for large vocabulary instance segmentation"
    - "self supervised surgical tool segmentation using kinematic information"
    - "kinematically informed interactive perception robot generated 3d models for classification"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "interactive perception leveraging action in perception and perception in action"
    - "mask r cnn"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"

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.CL(Computation and Language)