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: "on the pitfalls of measuring emergent communication"
  Abstract: How do we know if communication is emerging in a multi-agent system? The vast majority of recent papers on emergent communication show that adding a communication channel leads to an increase in reward or task success. This is a useful indicator, but provides only a coarse measure of the agent's learned communication abilities. As we move towards more complex environments, it becomes imperative to have a set of finer tools that allow qualitative and quantitative insights into the emergence of communication. This may be especially useful to allow humans to monitor agents' behaviour, whether for fault detection, assessing performance, or even building trust. In this paper, we examine a few intuitive existing metrics for measuring communication, and show that they can be misleading. Specifically, by training deep reinforcement learning agents to play simple matrix games augmented with a communication channel, we find a scenario where agents appear to communicate (their messages provide information about their subsequent action), and yet the messages do not impact the environment or other agent in any way. We explain this phenomenon using ablation studies and by visualizing the representations of the learned policies. We also survey some commonly used metrics for measuring emergent communication, and provide recommendations as to when these metrics should be used.

Connected through hyperedge HE-1 (induced by source paper "on the interaction between supervision and self play in emergent communication"):
  with members:
    - "anti efficient encoding in emergent communication"
    - "emergent linguistic phenomena in multi agent communication games"
    - "capacity bandwidth and compositionality in emergent language learning"
    - "ease of teaching and language structure from emergent communication"
    - "mastering emergent language learning to guide in simulated navigation"
    - "countering language drift via visual grounding"
    - "emergence of compositional language with deep generational transmission"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "emergent communication with world models"):
  with members:
    - "biases for emergent communication in multi agent reinforcement learning"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "tarmac targeted multi agent communication"
    - "learning when to communicate at scale in multiagent cooperative and competitive tasks"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "learning latent dynamics for planning from pixels"
    - "compositional obverter communication learning from raw visual input"
    - "world models"

Connected through hyperedge HE-3 (induced by source paper "incentivizing the emergence of grounded discrete communication between general agents"):
  with members:
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "compositional obverter communication learning from raw visual input"
    - "scalable agent alignment via reward modeling a research direction"
    - "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"
    - "natural language does not emerge naturally in multi agent dialog"
    - "emergence of grounded compositional language in multi agent populations"
    - "film visual reasoning with a general conditioning layer"

Connected through hyperedge HE-4 (induced by source paper "on emergent communication in competitive multi agent teams"):
  with members:
    - "measuring compositionality in representation learning"
    - "strong and simple baselines for multimodal utterance embeddings"
    - "emergence of compositional language with deep generational transmission"
    - "multimodal language analysis with recurrent multistage fusion"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "found in translation learning robust joint representations by cyclic translations between modalities"
    - "emergence of communication in an interactive world with consistent speakers"
    - "how agents see things on visual representations in an emergent language game"

Connected through hyperedge HE-5 (induced by source paper "signal instructed coordination in cooperative multi agent reinforcement learning"):
  with members:
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "correlation in extensive form games saddle point formulation and benchmarks"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "tarmac targeted multi agent communication"
    - "actor attention critic for multi agent reinforcement learning"
    - "multi agent common knowledge reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "entropy minimization in emergent languages"):
  with members:
    - "anti efficient encoding in emergent communication"
    - "emergent linguistic phenomena in multi agent communication games"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "compositional obverter communication learning from raw visual input"
    - "the conditional entropy bottleneck"
    - "how agents see things on visual representations in an emergent language game"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergent communication in a multi modal multi step referential game"

Connected through hyperedge HE-7 (induced by source paper "on memory mechanism in multi agent reinforcement learning"):
  with members:
    - "r maddpg for partially observable environments and limited 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"
    - "counterfactual multi agent policy gradients"

Connected through hyperedge HE-8 (induced by source paper "mastering emergent language learning to guide in simulated navigation"):
  with members:
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "learning to understand goal specifications by modelling reward"
    - "interactive grounded language acquisition and generalization in a 2d world"
    - "guiding policies with language via meta learning"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "home a household multimodal environment"

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.LG(Machine Learning)