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 modular control for embodied question answering"
  Abstract: We present a modular approach for learning policies for navigation over long planning horizons from language input. Our hierarchical policy operates at multiple timescales, where the higher-level master policy proposes subgoals to be executed by specialized sub-policies. Our choice of subgoals is compositional and semantic, i.e. they can be sequentially combined in arbitrary orderings, and assume human-interpretable descriptions (e.g. 'exit room', 'find kitchen', 'find refrigerator', etc.). #R##N#We use imitation learning to warm-start policies at each level of the hierarchy, dramatically increasing sample efficiency, followed by reinforcement learning. Independent reinforcement learning at each level of hierarchy enables sub-policies to adapt to consequences of their actions and recover from errors. Subsequent joint hierarchical training enables the master policy to adapt to the sub-policies. #R##N#On the challenging EQA (Das et al., 2018) benchmark in House3D (Wu et al., 2018), requiring navigating diverse realistic indoor environments, our approach outperforms prior work by a significant margin, both in terms of navigation and question answering.

Connected through hyperedge HE-1 (induced by source paper "an exploration of embodied visual exploration"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "where to look next unsupervised active visual exploration on 360 input"
    - "embodied visual recognition"
    - "the replica dataset a digital replica of indoor spaces"
    - "emergence of exploratory look around behaviors through active observation completion"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "learning exploration policies for navigation"

Connected through hyperedge HE-2 (induced by source paper "from seeing to moving a survey on learning for visual indoor navigation vin"):
  with members:
    - "walking with mind mental imagery enhanced embodied qa"
    - "what should i do now marrying reinforcement learning and symbolic planning"
    - "are we making real progress in simulated environments measuring the sim2real gap in embodied visual navigation"
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "graph attention memory for visual navigation"
    - "vision language navigation with self supervised auxiliary reasoning tasks"
    - "multi target embodied question answering"
    - "learning your way without map or compass panoramic target driven visual navigation"

Connected through hyperedge HE-3 (induced by source paper "mqa answering the question via robotic manipulation"):
  with members:
    - "improving generative visual dialog by answering diverse questions"
    - "mechanical search multi step retrieval of a target object occluded by clutter"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "interactive perception leveraging action in perception and perception in action"
    - "tgif qa toward spatio temporal reasoning in visual question answering"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "iqa visual question answering in interactive environments"
    - "embodied question answering"

Connected through hyperedge HE-4 (induced by source paper "audio visual embodied navigation"):
  with members:
    - "two body problem collaborative visual task completion"
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "do autonomous agents benefit from hearing"
    - "the replica dataset a digital replica of indoor spaces"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "co separating sounds of visual objects"
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "interactive gibson benchmark for interactive navigation in cluttered environments"

Connected through hyperedge HE-5 (induced by source paper "a survey of reinforcement learning informed by natural language"):
  with members:
    - "assessing bert s syntactic abilities"
    - "learning to generalize from sparse and underspecified rewards"
    - "using natural language for reward shaping in reinforcement learning"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "hierarchical decision making by generating and following natural language instructions"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "an algorithmic perspective on imitation learning"

Connected through hyperedge HE-6 (induced by source paper "bayesian relational memory for semantic visual navigation"):
  with members:
    - "a behavioral approach to visual navigation with graph localization networks"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "composable planning with attributes"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "iterative visual reasoning beyond convolutions"
    - "learning plannable representations with causal infogan"

Connected through hyperedge HE-7 (induced by source paper "embodied visual recognition"):
  with members:
    - "learning instance segmentation by interaction"
    - "yolov3 an incremental improvement"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "gibson env real world perception for embodied agents"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "active object perceiver recognition guided policy learning for object searching on mobile robots"
    - "learning to see the invisible end to end trainable amodal instance segmentation"

Connected through hyperedge HE-8 (induced by source paper "two body problem collaborative visual task completion"):
  with members:
    - "tarmac targeted multi agent communication"
    - "understanding grounded language learning agents"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "unifying map and landmark based representations for visual navigation"
    - "home a household multimodal environment"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "stabilising experience replay for deep multi agent 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.AI(Artificial Intelligence)