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: "event representations with tensor based compositions"
  Abstract: Robust and flexible event representations are important to many core areas in language understanding. Scripts were proposed early on as a way of representing sequences of events for such understanding, and has recently attracted renewed attention. However, obtaining effective representations for modeling script-like event sequences is challenging. It requires representations that can capture event-level and scenario-level semantics. We propose a new tensor-based composition method for creating event representations. The method captures more subtle semantic interactions between an event and its entities and yields representations that are effective at multiple event-related tasks. With the continuous representations, we also devise a simple schema generation method which produces better schemas compared to a prior discrete representation based method. Our analysis shows that the tensors capture distinct usages of a predicate even when there are only subtle differences in their surface realizations.

Connected through hyperedge HE-1 (induced by source paper "tensor decompositions in deep learning"):
  with members:
    - "theoretically expressive and edge aware graph learning"
    - "tucker tensor layer in fully connected neural networks"
    - "a fair comparison of graph neural networks for graph classification"
    - "bayesian tensor factorisation for bottom up hidden tree markov models"
    - "a gentle introduction to deep learning for graphs"
    - "contextual graph markov model a deep and generative approach to graph processing"
    - "how powerful are graph neural networks"
    - "mutan multimodal tucker fusion for visual question answering"

Connected through hyperedge HE-2 (induced by source paper "conversational response re ranking based on event causality and role factored tensor event embedding"):
  with members:
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "conversational ai the science behind the alexa prize"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "effective approaches to attention based neural machine translation"
    - "a neural conversational model"
    - "a diversity promoting objective function for neural conversation models"

Connected through hyperedge HE-3 (induced by source paper "structured minimally supervised learning for neural relation extraction"):
  with members:
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "combining distant and direct supervision for neural relation extraction"
    - "weakly supervised relation extraction by pattern enhanced embedding learning"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "globally normalized transition based neural networks"
    - "neural architectures for named entity recognition"
    - "noise mitigation for neural entity typing and relation extraction"
    - "multilingual relation extraction using compositional universal schema"

Connected through hyperedge HE-4 (induced by source paper "event representation learning enhanced with external commonsense knowledge"):
  with members:
    - "story ending prediction by transferable bert"
    - "deep contextualized word representations"
    - "constructing narrative event evolutionary graph for script event prediction"
    - "deep biaffine attention for neural dependency parsing"
    - "a study of entanglement in a categorical framework of natural language"
    - "learning semantic script knowledge with event embeddings"
    - "predicting abnormal returns from news using text classification"

Connected through hyperedge HE-5 (induced by source paper "picking apart story salads"):
  with members:
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "deep contextualized word representations"
    - "modeling semantic plausibility by injecting world knowledge"
    - "implicit argument prediction with event knowledge"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "learning paraphrastic sentence embeddings from back translated bitext"
    - "a structured self attentive sentence embedding"
    - "revisiting recurrent networks for paraphrastic sentence embeddings"

Connected through hyperedge HE-6 (induced by source paper "hierarchical quantized representations for script generation"):
  with members:
    - "toward controlled generation of text"
    - "learning hierarchical features from generative models"
    - "neural discrete representation learning"
    - "two discourse driven language models for semantics"
    - "effective approaches to attention based neural machine translation"
    - "generating sentences from a continuous space"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "event schema induction using tensor factorization with back off"):
  with members:
    - "cross sentence n ary relation extraction with graph lstms"
    - "relation schema induction using tensor factorization with side information"
    - "neural semantic role labeling with dependency path embeddings"
    - "probabilistic frame induction"

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)