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: "abcnn attention based convolutional neural network for modeling sentence pairs"
  Abstract: How to model a pair of sentences is a critical issue in many NLP tasks such as answer selection (AS), paraphrase identification (PI) and textual entailment (TE). Most prior work (i) deals with one individual task by fine-tuning a specific system; (ii) models each sentence's representation separately, rarely considering the impact of the other sentence; or (iii) relies fully on manually designed, task-specific linguistic features. This work presents a general Attention Based Convolutional Neural Network (ABCNN) for modeling a pair of sentences. We make three contributions. (i) ABCNN can be applied to a wide variety of tasks that require modeling of sentence pairs. (ii) We propose three attention schemes that integrate mutual influence between sentences into CNN; thus, the representation of each sentence takes into consideration its counterpart. These interdependent sentence pair representations are more powerful than isolated sentence representations. (iii) ABCNN achieves state-of-the-art performance on AS, PI and TE tasks.

Connected through hyperedge HE-1 (induced by source paper "rl duet online music accompaniment generation using deep reinforcement learning"):
  with members:
    - "the bach doodle approachable music composition with machine learning at scale"
    - "counterpoint by convolution"
    - "gansynth adversarial neural audio synthesis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "deep learning techniques for music generation a survey"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "an actor critic algorithm for sequence prediction"

Connected through hyperedge HE-2 (induced by source paper "cnntop a cnn based trajectory owner prediction method"):
  with members:
    - "comparative study of cnn and rnn for natural language processing"
    - "convolutional sequence to sequence learning"
    - "node2vec scalable feature learning for networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "revisiting paraphrase question generator using pairwise discriminator"):
  with members:
    - "learning semantic sentence embeddings using sequential pair wise discriminator"
    - "toward controlled generation of text"
    - "learning to ask neural question generation for reading comprehension"
    - "adversarial learning for neural dialogue generation"
    - "long text generation via adversarial training with leaked information"
    - "a deep generative framework for paraphrase generation"
    - "paraphrase generation with deep reinforcement learning"
    - "recurrent topic transition gan for visual paragraph generation"

Connected through hyperedge HE-4 (induced by source paper "two headed monster and crossed co attention networks"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "the evolved transformer"
    - "context aware self attention networks"
    - "modeling recurrence for transformer"
    - "adaptive attention span in transformers"
    - "scaling neural machine translation"
    - "modeling localness for self attention networks"

Connected through hyperedge HE-5 (induced by source paper "mrnn a multi resolution neural network with duplex attention for document retrieval in the context of question answering"):
  with members:
    - "a multi resolution word embedding for document retrieval from large unstructured knowledge bases"
    - "training a ranking function for open domain question answering"
    - "deep contextualized word representations"
    - "deep relevance ranking using enhanced document query interactions"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "text embeddings for retrieval from a large knowledge base"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-6 (induced by source paper "learning to selectively transfer reinforced transfer learning for deep text matching"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "reinforced co training"
    - "end task oriented textual entailment via deep explorations of inter sentence interactions"
    - "learning sampling policies for domain adaptation"
    - "reinforcement learning for relation classification from noisy data"
    - "alime assist an intelligent assistant for creating an innovative e commerce experience"
    - "response ranking with deep matching networks and external knowledge in information seeking conversation systems"
    - "learning how to active learn a deep reinforcement learning approach"

Connected through hyperedge HE-7 (induced by source paper "simple and effective text matching with richer alignment features"):
  with members:
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "stochastic answer networks for natural language inference"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "natural language inference over interaction space"
    - "neural paraphrase identification of questions with noisy pretraining"
    - "bilateral multi perspective matching for natural language sentences"
    - "a decomposable attention model for natural language inference"
    - "enhanced lstm for natural language inference"

Connected through hyperedge HE-8 (induced by source paper "falcon fast and lightweight convolution for compressing and accelerating cnn"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "towards the limit of network quantization"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "pruning convolutional neural networks for resource efficient inference"

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)