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: "correlating neural and symbolic representations of language"
  Abstract: Analysis methods which enable us to better understand the representations and functioning of neural models of language are increasingly needed as deep learning becomes the dominant approach in NLP. Here we present two methods based on Representational Similarity Analysis (RSA) and Tree Kernels (TK) which allow us to directly quantify how strongly the information encoded in neural activation patterns corresponds to information represented by symbolic structures such as syntax trees. We first validate our methods on the case of a simple synthetic language for arithmetic expressions with clearly defined syntax and semantics, and show that they exhibit the expected pattern of results. We then apply our methods to correlate neural representations of English sentences with their constituency parse trees.

Connected through hyperedge HE-1 (induced by source paper "discovering the compositional structure of vector representations with role learning networks"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "the compositionality of neural networks integrating symbolism and connectionism"
    - "measuring compositionality in representation learning"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "analyzing machine learned representations a natural language case study"
    - "blackbox meets blackbox representational similarity and stability analysis of neural language models and brains"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

Connected through hyperedge HE-2 (induced by source paper "probing natural language inference models through semantic fragments"):
  with members:
    - "can neural networks understand monotonicity reasoning"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "monalog a lightweight system for natural language inference based on monotonicity"
    - "assessing bert s syntactic abilities"
    - "inoculation by fine tuning a method for analyzing challenge datasets"
    - "a logical based corpus for cross lingual evaluation"
    - "posing fair generalization tasks for natural language inference"
    - "investigating bert s knowledge of language five analysis methods with npis"

Connected through hyperedge HE-3 (induced by source paper "a simple recurrent unit with reduced tensor product representations"):
  with members:
    - "dynamic evaluation of transformer language models"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "frage frequency agnostic word representation"
    - "deep contextualized word representations"
    - "can neural networks understand logical entailment"
    - "learning to reason with third order tensor products"
    - "glomo unsupervisedly learned relational graphs as transferable representations"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"

Connected through hyperedge HE-4 (induced by source paper "berts of a feather do not generalize together large variability in generalization across models with similar test set performance"):
  with members:
    - "what bert is not lessons from a new suite of psycholinguistic diagnostics for language models"
    - "visualizing and measuring the geometry of bert"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "multi task deep neural networks for natural language understanding"
    - "open sesame getting inside bert s linguistic knowledge"
    - "blackbox meets blackbox representational similarity and stability analysis of neural language models and brains"

Connected through hyperedge HE-5 (induced by source paper "higher order comparisons of sentence encoder representations"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "blackbox meets blackbox representational similarity and stability analysis of neural language models and brains"
    - "symbolic inductive bias for visually grounded learning of spoken language"
    - "the natural language decathlon multitask learning as question answering"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "how agents see things on visual representations in an emergent language game"

Connected through hyperedge HE-6 (induced by source paper "blackbox meets blackbox representational similarity and stability analysis of neural language models and brains"):
  with members:
    - "robust evaluation of language brain encoding experiments"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "gromov wasserstein alignment of word embedding spaces"
    - "deep contextualized word representations"
    - "towards understanding learning representations to what extent do different neural networks learn the same representation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "experiential distributional and dependency based word embeddings have complementary roles in decoding brain activity"

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)