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: "learning a neural network based representation for open set recognition"
  Abstract: Open set recognition problems exist in many domains. For example in security, new malware classes emerge regularly; therefore malware classification systems need to identify instances from unknown classes in addition to discriminating between known classes. In this paper we present a neural network based representation for addressing the open set recognition problem. In this representation instances from the same class are close to each other while instances from different classes are further apart, resulting in statistically significant improvement when compared to other approaches on three datasets from two different domains.

Connected through hyperedge HE-1 (induced by source paper "recent advances in open set recognition a survey"):
  with members:
    - "open world learning and application to product classification"
    - "large scale long tailed recognition in an open world"
    - "c2ae class conditioned auto encoder for open set recognition"
    - "bayesian semantic instance segmentation in open set world"
    - "open category detection with pac guarantees"
    - "open set domain adaptation by backpropagation"
    - "reducing network agnostophobia"
    - "learning factorized representations for open set domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "distribution networks for open set learning"):
  with members:
    - "semantic autoencoder for zero shot learning"
    - "prototypical networks for few shot learning"
    - "toward open set face recognition"
    - "fearnet brain inspired model for incremental learning"
    - "learning a deep embedding model for zero shot learning"
    - "matching networks for one shot learning"
    - "towards open set deep networks"
    - "towards open world recognition"

Connected through hyperedge HE-3 (induced by source paper "microsoft malware classification challenge"):
  with members:
    - "lempel ziv jaccard distance an effective alternative to ssdeep and sdhash"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "deepcloak masking deep neural network models for robustness against adversarial samples"
    - "imbalanced malware images classification a cnn based approach"
    - "itect scalable information theoretic similarity for malware detection"
    - "on the feasibility of malware authorship attribution"
    - "building better detection with privileged information"
    - "a theoretical framework for robustness of deep classifiers against adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "collective decision for open set recognition"):
  with members:
    - "open world learning and application to product classification"
    - "unseen class discovery in open world classification"
    - "the extreme value machine"
    - "doc deep open classification of text documents"
    - "adversarial robustness softmax versus openmax"
    - "sparse representation based open set recognition"
    - "generative openmax for multi class open set classification"
    - "towards open set deep networks"

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)