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 semantic encoders"
  Abstract: We present a memory augmented neural network for natural language understanding: Neural Semantic Encoders (NSE). NSE has a variable sized encoding memory that evolves over time and maintains the understanding of input sequences through read, compose and write operations. NSE can access multiple and shared memories depending on the complexity of a task. We demonstrated the effectiveness and the flexibility of NSE on five different natural language tasks, natural language inference, question answering, sentence classification, document sentiment analysis and machine translation where NSE achieved state-of-the-art performance when evaluated on publically available benchmarks. For example, our shared-memory model showed an encouraging result on neural machine translation, improving an attention-based baseline by approximately 1.0 BLEU.

Connected through hyperedge HE-1 (induced by source paper "multi level head wise match and aggregation in transformer for textual sequence matching"):
  with members:
    - "multi step retriever reader interaction for scalable open domain question answering"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "phrase indexed question answering a new challenge for scalable document comprehension"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "exploiting human social cognition for the detection of fake and fraudulent faces via memory networks"):
  with members:
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "faceforensics learning to detect manipulated facial images"
    - "recurrent convolutional strategies for face manipulation detection in videos"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "mesonet a compact facial video forgery detection network"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "pedestrian trajectory prediction with structured memory hierarchies"

Connected through hyperedge HE-3 (induced by source paper "metalearned neural memory"):
  with members:
    - "contextual memory trees"
    - "learning attractor dynamics for generative memory"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "learning to reason with third order tensor products"
    - "metalearning with hebbian fast weights"
    - "one shot visual imitation learning via meta learning"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "rapid adaptation with conditionally shifted neurons"

Connected through hyperedge HE-4 (induced by source paper "make up your mind adversarial generation of inconsistent natural language explanations"):
  with members:
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "annotation artifacts in natural language inference data"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "textual explanations for self driving vehicles"
    - "e snli natural language inference with natural language explanations"
    - "adversarially regularising neural nli models to integrate logical background knowledge"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-5 (induced by source paper "understanding roles and entities datasets and models for natural language inference"):
  with members:
    - "paws paraphrase adversaries from word scrambling"
    - "multi task deep neural networks for natural language understanding"
    - "annotation artifacts in natural language inference data"
    - "transforming question answering datasets into natural language inference datasets"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "large scale qa srl parsing"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a broad coverage challenge corpus for sentence understanding through inference"

Connected through hyperedge HE-6 (induced by source paper "label aware document representation via hybrid attention for extreme multi label text classification"):
  with members:
    - "attentionxml extreme multi label text classification with multi label attention based recurrent neural networks"
    - "sgm sequence generation model for multi label classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "explicit interaction model towards text classification"
    - "deep extreme multi label learning"
    - "a structured self attentive sentence embedding"
    - "node2vec scalable feature learning for networks"
    - "text classification improved by integrating bidirectional lstm with two dimensional max pooling"

Connected through hyperedge HE-7 (induced by source paper "controllable sentence simplification employing syntactic and lexical constraints"):
  with members:
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "sentence simplification with memory augmented neural networks"
    - "quickedit editing text translations by crossing words out"
    - "attention is all you need"
    - "sentence simplification with deep reinforcement learning"
    - "controlling linguistic style aspects in neural language generation"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-8 (induced by source paper "forecasting future action sequences with neural memory networks"):
  with members:
    - "coupled generative adversarial network for continuous fine grained action segmentation"
    - "anticipation and next action forecasting in video an end to end model with memory"
    - "when will you do what anticipating temporal occurrences of activities"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "tree memory networks for modelling long term temporal dependencies"
    - "encouraging lstms to anticipate actions very early"
    - "red reinforced encoder decoder networks for action anticipation"
    - "two stream lstm a deep fusion framework for human action recognition"

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.LG(Machine Learning)