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: "enhancing freebase question answering using textual evidence"
  Abstract: Existing knowledge-based question answering systems often rely on small annotated training data. While shallow methods like information extraction techniques are robust to data scarcity, they are less expressive than deep understanding methods, thereby failing at answering questions involving multiple constraints. Here we alleviate this problem by empowering a relation extraction method with additional evidence from Wikipedia. We first present a novel neural network based relation extractor to retrieve the candidate answers from Freebase, and then develop a refinement model to validate answers using Wikipedia. We achieve 53.3 F1 on WebQuestions, a substantial improvement over the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "representation learning for words and entities"):
  with members:
    - "deep multi view learning to rank"
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "deep contextualized word representations"
    - "deep sets"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "representation learning of entities and documents from knowledge base descriptions"
    - "new embedded representations and evaluation protocols for inferring transitive relations"
    - "neural collaborative filtering"

Connected through hyperedge HE-2 (induced by source paper "bert post training for review reading comprehension and aspect based sentiment analysis"):
  with members:
    - "exploiting coarse to fine task transfer for aspect level sentiment classification"
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "exploiting document knowledge for aspect level sentiment classification"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"
    - "coqa a conversational question answering challenge"
    - "drcd a chinese machine reading comprehension dataset"

Connected through hyperedge HE-3 (induced by source paper "review conversational reading comprehension"):
  with members:
    - "sdnet contextualized attention based deep network for conversational question answering"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"
    - "coqa a conversational question answering challenge"
    - "drcd a chinese machine reading comprehension dataset"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "quac question answering in context"

Connected through hyperedge HE-4 (induced by source paper "multi task learning for conversational question answering over a large scale knowledge base"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "complex sequential question answering towards learning to converse over linked question answer pairs with a knowledge graph"
    - "coarse to fine decoding for neural semantic parsing"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "attention is all you need"
    - "abstract syntax networks for code generation and semantic parsing"
    - "learning structured natural language representations for semantic parsing"
    - "a syntactic neural model for general purpose code generation"

Connected through hyperedge HE-5 (induced by source paper "bidirectional attentive memory networks for question answering over knowledge bases"):
  with members:
    - "exploiting rich syntactic information for semantic parsing with graph to sequence model"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "universal semantic parsing"
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "bidirectional attention flow for machine comprehension"
    - "s mart novel tree based structured learning algorithms applied to tweet entity linking"
    - "key value memory networks for directly reading documents"

Connected through hyperedge HE-6 (induced by source paper "learning to answer ambiguous questions with knowledge graph"):
  with members:
    - "question answering over freebase via attentive rnn with similarity matrix based cnn"
    - "simplequestions nearly solved a new upperbound and baseline approach"
    - "improved neural relation detection for knowledge base question answering"
    - "character level question answering with attention"
    - "question answering on freebase via relation extraction and textual evidence"
    - "large scale simple question answering with memory networks"
    - "question answering with subgraph embeddings"

Connected through hyperedge HE-7 (induced by source paper "explicit reasoning over end to end neural architectures for visual question answering"):
  with members:
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "why should i trust you explaining the predictions of any classifier"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "hierarchical question image co attention for visual question answering"
    - "visual question answering a survey of methods and datasets"
    - "fvqa fact based visual question answering"
    - "visual relationship detection with language priors"
    - "visual7w grounded question answering in images"

Connected through hyperedge HE-8 (induced by source paper "comparative study of cnn and rnn for natural language processing"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "learning text representation using recurrent convolutional neural network with highway layers"
    - "language modeling with gated convolutional networks"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "a large annotated corpus for learning natural language inference"
    - "traversing knowledge graphs in vector space"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "distributed representations of sentences and documents"

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)