human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "word order biases in deep agent emergent communication"):
  Abstract: Sequence-processing neural networks led to remarkable progress on many NLP tasks. As a consequence, there has been increasing interest in understanding to what extent they process language as humans do. We aim here to uncover which biases such models display with respect to "natural" word-order constraints. We train models to communicate about paths in a simple gridworld, using miniature languages that reflect or violate various natural language trends, such as the tendency to avoid redundancy or to minimize long-distance dependencies. We study how the controlled characteristics of our miniature languages affect individual learning and their stability across multiple network generations. The results draw a mixed picture. On the one hand, neural networks show a strong tendency to avoid long-distance dependencies. On the other hand, there is no clear preference for the efficient, non-redundant encoding of information that is widely attested in natural language. We thus suggest inoculating a notion of "effort" into neural networks, as a possible way to make their linguistic behavior more human-like.

Includes members:
  Member "on the convergence of adam and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "compositional obverter communication learning from raw visual input":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "emergent communication with world models")
  Member "how agents see things on visual representations in an emergent language game":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards graph representation learning in emergent communication")
  Member "emergence of linguistic communication from referential games with symbolic and pixel input":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the interaction between supervision and self play in emergent communication")
  Member "emergent communication in a multi modal multi step referential game":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the interaction between supervision and self play in emergent communication")
  Member "emergence of language with multi agent games learning to communicate with sequences of symbols":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the interaction between supervision and self play in emergent communication")
  Member "natural language does not emerge naturally in multi agent dialog":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the interaction between supervision and self play in emergent communication")
  Member "emergence of grounded compositional language in multi agent populations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "improving coordination in small scale multi agent deep reinforcement learning through memory driven communication")

Question: Please tell me which class the source paper 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)