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: "deep residual learning for weakly supervised relation extraction"
  Abstract: Deep residual learning (ResNet) is a new method for training very deep neural networks using identity map-ping for shortcut connections. ResNet has won the ImageNet ILSVRC 2015 classification task, and achieved state-of-the-art performances in many computer vision tasks. However, the effect of residual learning on noisy natural language processing tasks is still not well understood. In this paper, we design a novel convolutional neural network (CNN) with residual learning, and investigate its impacts on the task of distantly supervised noisy relation extraction. In contradictory to popular beliefs that ResNet only works well for very deep networks, we found that even with 9 layers of CNNs, using identity mapping could significantly improve the performance for distantly-supervised relation extraction.

Connected through hyperedge HE-1 (induced by source paper "long tail relation extraction via knowledge graph embeddings and graph convolution networks"):
  with members:
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "reinforcement learning for relation classification from noisy data"
    - "graph convolutional networks for classification with a structured label space"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "dependency parsing with dilated iterated graph cnns"
    - "incorporating relation paths in neural relation extraction"
    - "jointly extracting relations with class ties via effective deep ranking"

Connected through hyperedge HE-2 (induced by source paper "neural symbolic argumentation mining an argument in favour of deep learning and reasoning"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "argument component classification for classroom discussions"
    - "multi task learning for argumentation mining in low resource settings"
    - "what is the essence of a claim cross domain claim identification"
    - "argument mining with structured svms and rnns"
    - "logic tensor networks deep learning and logical reasoning from data and knowledge"
    - "defeasible logic programming an argumentative approach"

Connected through hyperedge HE-3 (induced by source paper "a bag of concepts model improves relation extraction in a narrow knowledge domain with limited data"):
  with members:
    - "convolutional neural networks for chemical disease relation extraction are improved with character based word embeddings"
    - "cross sentence n ary relation extraction with graph lstms"
    - "combining neural networks and log linear models to improve relation extraction"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "convolutional neural networks for chemical disease relation extraction are improved with character based word embeddings"):
  with members:
    - "relation extraction a survey"
    - "attention is all you need"
    - "attending to all mention pairs for full abstract biological relation extraction"
    - "a novel neural network model for joint pos tagging and graph based dependency parsing"
    - "incorporating relation paths in neural relation extraction"
    - "distant supervision for relation extraction beyond the sentence boundary"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "quasi recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "combining distant and direct supervision for neural relation extraction"):
  with members:
    - "allennlp a deep semantic natural language processing platform"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "zero shot sequence labeling transferring knowledge from sentences to tokens"
    - "neural relation extraction via inner sentence noise reduction and transfer learning"
    - "multi level structured self attentions for distantly supervised relation extraction"
    - "learning with noise enhance distantly supervised relation extraction with dynamic transition matrix"
    - "neural discourse structure for text categorization"
    - "jointly extracting relations with class ties via effective deep ranking"

Connected through hyperedge HE-6 (induced by source paper "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "prototypical networks for few shot learning"
    - "learning with noise enhance distantly supervised relation extraction with dynamic transition matrix"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "incorporating relation paths in neural relation extraction"
    - "matching networks for one shot learning"
    - "improved relation extraction with feature rich compositional embedding models"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-7 (induced by source paper "cross relation cross bag attention for distantly supervised relation extraction"):
  with members:
    - "learning with noise enhance distantly supervised relation extraction with dynamic transition matrix"
    - "heterogeneous supervision for relation extraction a representation learning approach"
    - "combining recurrent and convolutional neural networks for relation classification"
    - "classifying relations by ranking with convolutional neural networks"
    - "semantic relation classification via convolutional neural networks with simple negative sampling"
    - "efficient estimation of word representations in vector space"
    - "improving neural networks by preventing co adaptation of feature detectors"

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)