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: "multi instance learning by treating instances as non i i d samples"
  Abstract: Multi-instance learning attempts to learn from a training set consisting of labeled bags each containing many unlabeled instances. Previous studies typically treat the instances in the bags as independently and identically distributed. However, the instances in a bag are rarely independent, and therefore a better performance can be expected if the instances are treated in an non-i.i.d. way that exploits the relations among instances. In this paper, we propose a simple yet effective multi-instance learning method, which regards each bag as a graph and uses a specific kernel to distinguish the graphs by considering the features of the nodes as well as the features of the edges that convey some relations among instances. The effectiveness of the proposed method is validated by experiments.

Connected through hyperedge HE-1 (induced by source paper "text length adaptation in sentiment classification"):
  with members:
    - "strong baselines for neural semi supervised learning under domain shift"
    - "a survey on deep transfer learning"
    - "aspect sentiment model for micro reviews"
    - "learning word vectors for 157 languages"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multiple instance learning networks for fine grained sentiment analysis"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "adversarial deep averaging networks for cross lingual sentiment classification"

Connected through hyperedge HE-2 (induced by source paper "a snapshot on nonstandard supervised learning problems taxonomy relationships problem transformations and algorithm adaptations"):
  with members:
    - "dealing with difficult minority labels in imbalanced mutilabel data sets"
    - "label distribution learning"
    - "multi instance multi label learning"
    - "multi label classification methods for multi target regression"

Connected through hyperedge HE-3 (induced by source paper "distributionally robust multi instance learning with stable instances"):
  with members:
    - "attention based deep multiple instance learning"
    - "stable prediction across unknown environments"
    - "causally regularized learning with agnostic data selection bias"

Connected through hyperedge HE-4 (induced by source paper "deep multiple instance feature learning via variational autoencoder"):
  with members:
    - "relaxed multiple instance svm with application to object discovery"

Connected through hyperedge HE-5 (induced by source paper "multiple instance learning a survey of problem characteristics and applications"):
  with members:
    - "feature learning from spectrograms for assessment of personality traits"
    - "risk minimization framework for multiple instance learning from positive and unlabeled bags"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "weakly supervised scalable audio content analysis"
    - "label stability in multiple instance learning"
    - "characterizing multiple instance datasets"
    - "a multi scale multiple instance video description network"
    - "detector discovery in the wild joint multiple instance and representation learning"

Connected through hyperedge HE-6 (induced by source paper "miml fcn multi instance multi label learning via fully convolutional networks with privileged information"):
  with members:
    - "identity mappings in deep residual networks"
    - "improving multi label learning with missing labels by structured semantic correlations"
    - "yfcc100m the new data in multimedia research"
    - "weakly supervised deep detection networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "exploit bounding box annotations for multi label object recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-7 (induced by source paper "bag level aggregation for multiple instance active learning in instance classification problems"):
  with members:
    - "multiple instance learning a survey of problem characteristics and applications"
    - "on measuring and quantifying performance error rates surrogate loss and an example in ssl"
    - "introducing geometry in active learning for image segmentation"
    - "a multi scale multiple instance video description network"
    - "from captions to visual concepts and back"
    - "deep visual semantic alignments for generating image descriptions"
    - "active learning with statistical models"

Connected through hyperedge HE-8 (induced by source paper "set to set hashing with applications in visual recognition"):
  with members:
    - "learning to hash for indexing big data a survey"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "hashing for similarity search a survey"
    - "a general two step approach to learning based hashing"
    - "multimodal similarity preserving hashing"
    - "compact hyperplane hashing with bilinear functions"

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)