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: "virtual embodiment a scalable long term strategy for artificial intelligence research"
  Abstract: Meaning has been called the "holy grail" of a variety of scientific disciplines, ranging from linguistics to philosophy, psychology and the neurosciences. The field of Artifical Intelligence (AI) is very much a part of that list: the development of sophisticated natural language semantics is a sine qua non for achieving a level of intelligence comparable to humans. Embodiment theories in cognitive science hold that human semantic representation depends on sensori-motor experience; the abundant evidence that human meaning representation is grounded in the perception of physical reality leads to the conclusion that meaning must depend on a fusion of multiple (perceptual) modalities. Despite this, AI research in general, and its subdisciplines such as computational linguistics and computer vision in particular, have focused primarily on tasks that involve a single modality. Here, we propose virtual embodiment as an alternative, long-term strategy for AI research that is multi-modal in nature and that allows for the kind of scalability required to develop the field coherently and incrementally, in an ethically responsible fashion.

Connected through hyperedge HE-1 (induced by source paper "learning to speak and act in a fantasy text adventure game"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "game based video context dialogue"
    - "textworld a learning environment for text based games"
    - "talk the walk navigating new york city through grounded dialogue"
    - "training millions of personalized dialogue agents"
    - "building generalizable agents with a realistic and rich 3d environment"

Connected through hyperedge HE-2 (induced by source paper "self educated language agent with hindsight experience replay for instruction following"):
  with members:
    - "language as an abstraction for hierarchical deep reinforcement learning"
    - "learning to follow directions in street view"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "visual hindsight experience replay"
    - "actrce augmenting experience via teacher s advice for multi goal reinforcement learning"
    - "competitive experience replay"
    - "a survey of reinforcement learning informed by natural language"
    - "babyai first steps towards grounded language learning with a human in the loop"

Connected through hyperedge HE-3 (induced by source paper "emergence of compositional language with deep generational transmission"):
  with members:
    - "compositional obverter communication learning from raw visual input"
    - "translating neuralese"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "large scale evolution of image classifiers"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "natural language does not emerge naturally in multi agent dialog"
    - "learning cooperative visual dialog agents with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "cracking the contextual commonsense code understanding commonsense reasoning aptitude of deep contextual representations"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "ernie enhanced language representation with informative entities"
    - "do neural language representations learn physical commonsense"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "linguistic knowledge and transferability of contextual representations"
    - "winogrande an adversarial winograd schema challenge at scale"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-5 (induced by source paper "talk the walk navigating new york city through grounded dialogue"):
  with members:
    - "active neural localization"
    - "a deep compositional framework for human like language acquisition in virtual environment"
    - "unifying map and landmark based representations for visual navigation"
    - "emergent communication in a multi modal multi step referential game"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "modulating early visual processing by language"
    - "learning visually grounded sentence representations"
    - "learning cooperative visual dialog agents with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "a new dataset and model for learning to understand navigational instructions"):
  with members:
    - "a deep compositional framework for human like language acquisition in virtual environment"
    - "gated attention architectures for task oriented language grounding"
    - "shapeworld a new test methodology for multimodal language understanding"
    - "deep learning evaluation using deep linguistic processing"
    - "grounded language learning in a simulated 3d world"
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "analyzing the behavior of visual question answering models"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"

Connected through hyperedge HE-7 (induced by source paper "emergent communication in a multi modal multi step referential game"):
  with members:
    - "translating neuralese"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "emergence of grounded compositional language in multi agent populations"
    - "end to end optimization of goal driven and visually grounded dialogue systems"
    - "guesswhat visual object discovery through multi modal dialogue"
    - "reasoning about pragmatics with neural listeners and speakers"
    - "building machines that learn and think like people"

Connected through hyperedge HE-8 (induced by source paper "mastering the dungeon grounded language learning by mechanical turker descent"):
  with members:
    - "a deep compositional framework for human like language acquisition in virtual environment"
    - "naturalizing a programming language via interactive learning"
    - "progressive neural networks"
    - "gated attention architectures for task oriented language grounding"
    - "grounded language learning in a simulated 3d world"
    - "learning language games through interaction"
    - "a roadmap towards machine intelligence"
    - "language understanding for text based games using 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.AI(Artificial Intelligence)