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 paradigm for situated and goal driven language learning"
  Abstract: A distinguishing property of human intelligence is the ability to flexibly use language in order to communicate complex ideas with other humans in a variety of contexts. Research in natural language dialogue should focus on designing communicative agents which can integrate themselves into these contexts and productively collaborate with humans. In this abstract, we propose a general situated language learning paradigm which is designed to bring about robust language agents able to cooperate productively with humans.

Connected through hyperedge HE-1 (induced by source paper "multi agent reinforcement learning as a computational tool for language evolution research historical context and future challenges"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "curious intrinsically motivated multi task multi goal reinforcement learning"
    - "emergent complexity via multi agent competition"
    - "emergence of grounded compositional language in multi agent populations"
    - "multi agent reinforcement learning in sequential social dilemmas"

Connected through hyperedge HE-2 (induced by source paper "from abstractions to natural languages for coordinating planning agents"):
  with members:
    - "translating neuralese"
    - "near optimal behavior via approximate state abstraction"
    - "emergence of grounded compositional language in multi agent populations"
    - "learning multiagent communication with backpropagation"
    - "synthesizing robust plans under incomplete domain models"
    - "the symbol grounding problem"

Connected through hyperedge HE-3 (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-4 (induced by source paper "countering language drift via visual grounding"):
  with members:
    - "lessons learned in multilingual grounded language learning"
    - "translating neuralese"
    - "imagination improves multimodal translation"
    - "emergent communication in a multi modal multi step referential game"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "learning visually grounded sentence representations"
    - "natural language does not emerge naturally in multi agent dialog"
    - "towards neural machine translation with partially aligned corpora"

Connected through hyperedge HE-5 (induced by source paper "on memory mechanism in multi agent reinforcement learning"):
  with members:
    - "r maddpg for partially observable environments and limited communication"
    - "on the pitfalls of measuring emergent communication"
    - "bayes tomop a fast detection and best response algorithm towards sophisticated opponents"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "compositional obverter communication learning from raw visual input"
    - "emergence of communication in an interactive world with consistent speakers"
    - "machine theory of mind"
    - "emergent communication in a multi modal multi step referential game"

Connected through hyperedge HE-6 (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-7 (induced by source paper "on the pitfalls of measuring emergent communication"):
  with members:
    - "understanding agent incentives using causal influence diagrams part i single action settings"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "compositional obverter communication learning from raw visual input"
    - "emergence of communication in an interactive world with consistent speakers"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergent communication through negotiation"
    - "emergent communication in a multi modal multi step referential game"

Connected through hyperedge HE-8 (induced by source paper "from abstractions to natural languages for planning agents"):
  with members:
    - "translating neuralese"
    - "distributed planning in hierarchical factored mdps"
    - "the symbol grounding problem"

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)