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: "neural sentence embedding using only in domain sentences for out of domain sentence detection in dialog systems"
  Abstract: Only in-domain sentences are used to train out-of-domain sentence detection.A long short-term memory network is used to extract features from sentences.Domain-category analysis is used as an auxiliary task.A two-channel approach is applied to word representations.An autoencoder is used for one-class classification. To ensure satisfactory user experience, dialog systems must be able to determine whether an input sentence is in-domain (ID) or out-of-domain (OOD). We assume that only ID sentences are available as training data because collecting enough OOD sentences in an unbiased way is a laborious and time-consuming job. This paper proposes a novel neural sentence embedding method that represents sentences in a low-dimensional continuous vector space that emphasizes aspects that distinguish ID cases from OOD cases. We first used a large set of unlabeled text to pre-train word representations that are used to initialize neural sentence embedding. Then we used domain-category analysis as an auxiliary task to train neural sentence embedding for OOD sentence detection. After the sentence representations were learned, we used them to train an autoencoder aimed at OOD sentence detection. We evaluated our method by experimentally comparing it to the state-of-the-art methods in an eight-domain dialog system; our proposed method achieved the highest accuracy in all tests.

Connected through hyperedge HE-1 (induced by source paper "out of domain detection for natural language understanding in dialog systems"):
  with members:
    - "open world learning and application to product classification"
    - "efficient large scale domain classification with personalized attention"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "learning from positive and unlabeled data a survey"
    - "deep anomaly detection using geometric transformations"
    - "deep anomaly detection with outlier exposure"
    - "generative probabilistic novelty detection with adversarial autoencoders"

Connected through hyperedge HE-2 (induced by source paper "out of domain detection for low resource text classification tasks"):
  with members:
    - "imposing label relational inductive bias for extremely fine grained entity typing"
    - "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"
    - "joint learning of domain classification and out of domain detection with dynamic class weighting for satisficing false acceptance rates"
    - "one shot relational learning for knowledge graphs"
    - "diverse few shot text classification with multiple metrics"
    - "doc deep open classification of text documents"
    - "prototypical networks for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-3 (induced by source paper "contextual out of domain utterance handling with counterfeit data augmentation"):
  with members:
    - "building a conversational agent overnight with dialogue self play"
    - "key value retrieval networks for task oriented dialogue"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "learning end to end goal oriented dialog"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-4 (induced by source paper "improving robustness of neural dialog systems in a data efficient way with turn dropout"):
  with members:
    - "challenging neural dialogue models with natural data memory networks fail on incremental phenomena"
    - "key value retrieval networks for task oriented dialogue"
    - "toward continual learning for conversational agents"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "learning end to end goal oriented dialog"
    - "generating sentences from a continuous space"

Connected through hyperedge HE-5 (induced by source paper "joint learning of domain classification and out of domain detection with dynamic class weighting for satisficing false acceptance rates"):
  with members:
    - "onenet joint domain intent slot prediction for spoken language understanding"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "lstm a search space odyssey"
    - "just ask building an architecture for extensible self service spoken language understanding"
    - "domain adaptation of recurrent neural networks for natural language understanding"
    - "adam a method for stochastic optimization"
    - "smote synthetic minority over sampling technique"

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)