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: "anti efficient encoding in emergent communication"
  Abstract: Despite renewed interest in emergent language simulations with neural networks, little is known about the basic properties of the induced code, and how they compare to human language. One fundamental characteristic of the latter, known as Zipf's Law of Abbreviation (ZLA), is that more frequent words are efficiently associated to shorter strings. We study whether the same pattern emerges when two neural networks, a "speaker" and a "listener", are trained to play a signaling game. Surprisingly, we find that networks develop an \emph{anti-efficient} encoding scheme, in which the most frequent inputs are associated to the longest messages, and messages in general are skewed towards the maximum length threshold. This anti-efficient code appears easier to discriminate for the listener, and, unlike in human communication, the speaker does not impose a contrasting least-effort pressure towards brevity. Indeed, when the cost function includes a penalty for longer messages, the resulting message distribution starts respecting ZLA. Our analysis stresses the importance of studying the basic features of emergent communication in a highly controlled setup, to ensure the latter will not strand too far from human language. Moreover, we present a concrete illustration of how different functional pressures can lead to successful communication codes that lack basic properties of human language, thus highlighting the role such pressures play in the latter.

Connected through hyperedge HE-1 (induced by source paper "on the interaction between supervision and self play in emergent communication"):
  with members:
    - "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"
    - "on the pitfalls of measuring 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 "capacity bandwidth and compositionality in emergent language learning"):
  with members:
    - "linguistic generalization and compositionality in modern artificial neural networks"
    - "on the interaction between supervision and self play in emergent communication"
    - "the compositionality of neural networks integrating symbolism and connectionism"
    - "word order biases in deep agent emergent communication"
    - "measuring compositionality in representation learning"
    - "countering language drift via visual grounding"
    - "compositional generalization through meta sequence to sequence learning"
    - "pytorch an imperative style high performance deep learning library"

Connected through hyperedge HE-3 (induced by source paper "entropy minimization in emergent languages"):
  with members:
    - "emergent linguistic phenomena in multi agent communication games"
    - "on the pitfalls of measuring emergent communication"
    - "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-4 (induced by source paper "egg a toolkit for research on emergence of language in games"):
  with members:
    - "word order biases in deep agent emergent communication"
    - "measuring compositionality in representation learning"
    - "on the pitfalls of measuring emergent communication"
    - "information minimization in emergent languages"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "how agents see things on visual representations in an emergent language game"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "optimal coding and the origins of zipfian laws"):
  with members:
    - "source codes in human communication"
    - "the polysemy of the words that children learn over time"
    - "the placement of the head that minimizes online memory a complex systems approach"

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)